How to Share a Secret, Infinitely

Size: px
Start display at page:

Download "How to Share a Secret, Infinitely"

Transcription

1 How to Share a Secret, Infntely Ilan Komargodsk Mon Naor Eylon Yogev Abstract Secret sharng schemes allow a dealer to dstrbute a secret pece of nformaton among several partes such that only qualfed subsets of partes can reconstruct the secret. The collecton of qualfed subsets s called an access structure. The best known example s the k-threshold access structure, where the qualfed subsets are those of sze at least k. When k = 2 and there are n partes, there are schemes for sharng an l-bt secret n whch the share sze of each party s roughly max{l, log n} bts, and ths s tght even for secrets of 1 bt. In these schemes, the number of partes n must be gven n advance to the dealer. In ths work we consder the case where the set of partes s not known n advance and could potentally be nfnte. Our goal s to gve the t th party arrvng the smallest possble share as a functon of t. Our man result s such a scheme for the k-threshold access structure and 1-bt secrets where the share sze of party t s (k 1) log t + poly(k) o(log t). For k = 2 we observe an equvalence to prefx codes and present matchng upper and lower bounds of the form log t + log log t + log log log t + O(1). Fnally, we show that for any access structure there exsts such a secret sharng scheme wth shares of sze 2 t 1. A prelmnary verson of ths paper appeared n the 14th Internatonal Conference on Theory of Cryptography (TCC 2016-B) [KNY16]. Department of Computer Scence and Appled Mathematcs, Wezmann Insttute of Scence Israel, Rehovot 76100, Israel. Emal: {lan.komargodsk,mon.naor,eylon.yogev}@wezmann.ac.l. Research supported n part by grants from the Israel Scence Foundaton (grants no. 1255/12 and 950/16), BSF and from the I-CORE Program of the Plannng and Budgetng Commttee and the Israel Scence Foundaton (grant no. 4/11). Mon Naor s the ncumbent of the Judth Kleeman Professoral Char. Ilan Komargodsk s supported n part by a Levzon fellowshp.

2 1 Introducton 640K ought to be enough for anybody Msattrbuted to Bll Gates, 1981 Secret sharng s a method by whch a secret pece of nformaton can be dstrbuted among n partes so that any qualfed subset of partes can reconstruct the secret, whle every unqualfed subset of partes learns nothng about the secret. The collecton of qualfed subsets s called an access structure. Secret sharng schemes are a basc prmtve and have found applcatons n cryptography and dstrbuted computng; see the extensve survey of Bemel [Be11]. A sgnfcant goal n secret sharng s to mnmze the share sze, namely, the amount of nformaton dstrbuted to the partes. Secret sharng schemes were ntroduced n the late 1970s by Shamr [Sha79] and Blakley [Bla79] for the k-out-of-n threshold access structures that ncludes all subsets of cardnalty at least k for 1 k n. Ther constructons are farly effcent both n the sze of the shares and n the computaton requred for sharng and reconstructon. Ito, Sato, and Nshzek [ISN93] showed the exstence of a secret sharng scheme for every (monotone) access structure. In ther scheme the sze of the shares s proportonal to the depth 2 complexty of the access structure when vewed as a Boolean functon (and hence shares are exponental for most structures). Benaloh and Lechter [BL88] gave a scheme wth share sze polynomal n the monotone formula complexty of the access structure. Karchmer and Wgderson [KW93] generalzed ths constructon so that the sze s polynomal n the monotone span program complexty. All of these schemes requre that an upper bound on the number of partcpants s known n advance. However, n many scenaros ths s ether unrealstc or prone to dsaster. Moreover, even f a crude upper bound n s known n advance, t s preferable to have shares as small as possble f the eventual number of partcpants s much smaller than ths bound on n. In ths work we consder the well motvated, yet almost unexplored 1, case where the set of partes s not known n advanced and could potentally be nfnte. Our goal s to gve the t th party arrvng the smallest possble share as a functon of t. We requre that n each round, as a new party arrves, there s no communcaton to the partes that have already receved shares,.e. the dealer dstrbutes a share only to the new party. We call such access structures evolvng: the partes arrve one by one and, n the most general case, a qualfed subset s revealed to the dealer only when all partes n that subset are present (n specal cases the dealer knows the access structure to begn wth, just does not have an upper bound on the number of partes). For ths to make sense, we assume that the changes to the access structure are monotone, namely, partes are only added and qualfed sets reman qualfed. Our frst result s a constructon of a secret sharng scheme for any evolvng access structure. 2 Theorem 1.1. For every evolvng access structure there s a secret sharng scheme for a 1-bt secret where the share sze of the t th party s 2 t 1. Then, we construct more effcent schemes for specfc access structures. We focus on the evolvng k-threshold access structure for k N, where at any pont n tme any k partes can reconstruct the secret but no k 1 partes can learn anythng about the secret. 1 But see the work of Csrmaz and Tardos [CT12] dscussed below. 2 A comparson wth the work of Csrmaz and Tardos [CT12] s gven below n Secton

3 Theorem 1.2. For every k, l N, there s a secret sharng scheme for the evolvng k-threshold access structure and an l-bt secret n whch for every t N the share sze of the t th party s (k 1) log t + poly(k, l) o(log t). For k = 2, we present a tght connecton to prefx codes for the ntegers. A prefx code s a code n whch no codeword s a prefx of any other codeword. These codes are wdely used, for example n country callng codes, the UTF-8 system for encodng Uncode characters, and more. Theorem 1.3. Let σ : N N. A prefx code for the ntegers n whch the length of the t th codeword s σ(t) exsts f and only f a secret sharng scheme for the evolvng 2-threshold access structure and 1-bt secret n whch the share sze of the t th party s σ(t). As a corollary, we get that there s a secret sharng scheme for the evolvng 2-threshold access structure and a 1-bt secret n whch the share sze of the t th party s log t+2 log log t+2. Moreover, ths s optmal: there s no secret sharng scheme for ths access structure n whch the share sze of the t th party s bounded by log t + log log t + O(1) bts. See Corollares 4.2 and 4.3 for the precse statements. 1.1 Dscusson Schemes for general access structures. In the classcal settng of secret sharng many schemes are known for general access structures, dependng on ther representaton [ISN93, BL88, KW93]. All of these schemes result wth shares of exponental sze for general access structures. One of the most mportant open problems n the area of secret sharng s to prove the necessty of long shares, namely, fnd an access structure (even a non-explct one) that requres exponental sze shares. Our scheme for general evolvng access structures also results wth exponental sze shares. Snce any access structure can be made evolvng, we cannot hope to obtan anythng better than exponental n general (unless we have a major breakthrough n the classcal settng). Threshold schemes. In the classcal settng there are several dfferent schemes for the threshold access structure. One of the best such schemes (n terms of the computaton needed for sharng and reconstructon and n terms of the share sze) s due to Shamr [Sha79]. In ths scheme, to share a 1-bt secret among n partes, roughly log n bts have to be dstrbuted to each party. It s known that log n bts are essentally requred, so Shamr s scheme s optmal (see [CCX13] for the orgnal proof of Klan and Nsan [KN90], an mprovement, and a dscusson of the hstory; see also [BGK16]). Let us revew Shamr s scheme for the k-out-of-n threshold access structure. The dealer holdng a secret bt s, samples a random polynomal p( ) of degree k 1 wth coeffcents over GF(q), where the free coeffcent s fxed to be s, and gves party [n] the feld element p(). The feld sze, q, s chosen to be the smallest prme (or a power of a prme) larger than n. Correctness of the scheme follows by the fact that k ponts on a polynomal of degree k 1 completely defne the polynomal and allow for computng p(0) = s. Securty follows by a countng argument showng that gven less than k ponts, both possbltes for the free coeffcent are equally lkely. The share of each party s an element n the feld GF(q) that can be represented usng log q log n bts. Notce that the share sze s ndependent of k. As a frst attempt one mght try to adapt ths procedure to the evolvng settng. But snce n s not fxed, what q should we choose? A natural dea s to use an extenson feld. Roughly, we 2

4 would smulate the dealer for Shamr s scheme, sample a random polynomal of degree k 1 and ncrease the feld sze from whch we compute shares as more partes arrve. Ideally, for the share of the t th party we wll use a feld of sze O(t). Ths mples that the share sze of party t would be log(o(t)) log t + log log t for large enough t. The lower bound n Corollary 4.3 means that no such soluton can work! We take a dfferent path for obtanng effcent schemes. For example, for k = 2, our scheme results wth optmal share sze (up to addtve lower order terms) for the t th party: the frst term s log t (wthout hdden constant factors) and there s an addtonal lower order term that depends on log log t. See Secton 4 for detals. Lnearty of our schemes. In a lnear scheme the secret s vewed as an element of a fnte feld, and the shares are obtaned by applyng a lnear mappng to the secret and several ndependent random feld elements. Equvalently, a lnear scheme s defned by requrng that each qualfed set reconstructs the secret by applyng a lnear functon to ts shares [Be96, Secton 4.1]. Most of the known schemes are lnear (see [BI01] for an excepton). Lnear schemes are very useful for updatng and manpulatng secret shares (cf. proactve secret sharng [HJKY95]) and have many applcatons, most notably for secure mult-party computaton [BGW88, CDM00]. Our schemes from Theorems 1.1 and 1.2 are lnear, whereas the scheme based on prefx codes from Theorem 1.3 s non-lnear. 1.2 Related Work The work of Csrmaz and Tardos. Most smlar to our settng s the noton of on-lne secret sharng of Csrmaz and Tardos [CT12]. Csrmaz and Tardos present a scheme for any evolvng access structure wth the restrcton that every party partcpates n at most d qualfed sets, where d s an upper bound known n advance. The share sze of every party n ther scheme s lnear n d. In comparson, Theorem 1.1 works wthout any such restrcton but the share sze s larger. In addton, Csrmaz and Tardos presented a scheme for the evolvng 2-threshold access structure n whch the share sze of party t s lnear n t. We use ths scheme as a base for our 2-threshold scheme (Theorem 1.2) whch gves an exponental mprovement on ther scheme. Other evolvng settngs. There are numerous areas where systems are desgned to work wthout any fxed upper bound on the sze or the duraton they wll be used. A few examples nclude prefx codes of the ntegers (a.k.a. prefx-free encodngs), such as the Elas code [El75] or the onlne encodng of Dods et al. [DPT10], labelng nodes for testng adjacency n possbly nfnte graphs [KNR92], forward-secure sgnatures wth an unbounded number of tme perods [MMM02], and data structures for approxmate set membershp (Bloom flters) for sets of unknown sze [PSW13]. Other connectons to prefx codes. There are other settng n whch problems were shown to be tghtly related to prefx codes. For example, Bentley and Yao [BY76] studed the unbounded search problem (.e. the problem of comparson-based search n an array of unbounded sze) and showed an effcent determnstc algorthm for the problem. Moreover, they observed that every such algorthm mples a prefx-free encodng of the ntegers. Another work wth related deas was of Even and Rodeh [ER78] who showed a method for nsertng delmters n a contguous sequence of strngs wthout addng new symbols and wth mnmal overhead. 3

5 1.3 Overvew of Our Constructons and Technques Frst, we overvew our constructon for general evolvng access structures. Then, we descrbe our constructon for the evolvng 2-threshold access structure. Ths serves as a warm-up for our more general constructon for k-threshold access structures. Lastly, we dscuss the connecton wth prefx codes. General evolvng access structures. Let A be any evolvng access structure and let A t = A [t] be the qualfed sets at tme t. Note that the dealer does not know A n advance but s only gven A t when the t th party arrves. Let s {0, 1} be the secret to be shared. The share of party t N conssts of 2 t 1 bts, each denoted by r A for A [t 1]. The r A s are generated as follows: f party t completes a mnmal qualfed set wth A = { 1,..., k }, namely A {t} A t, then the dealer gves party t the bt r A {t} = r {1 }... r {1,..., k } s. Otherwse, f A {t} s unqualfed, then the dealer sets r A {t} {0, 1} to be a unformly random bt. Thus, by XORng the approprate shares a qualfed set can recover s. Detals can be found n Secton 3. Prefx codes and evolvng 2-threshold. Gven any prefx code n whch the length of the t th codeword s σ(t), we construct a secret sharng scheme for the evolvng 2-threshold access structure n whch the share sze of the t th party s σ(t). The dealer mantans an nfntely (random) growng strng w and gves the t th party arrvng ether the strng w[1 : σ(t)] (f the secret to share s 0), or w[1 : σ(t)] Σ(t), where w[1 : σ(t)] s the σ(t)-bt length prefx of w and Σ(t) s the encodng of the number t usng the prefx code. One can verfy that each sngle share does not leak any nformaton about the secret whle any two partes can together decde f one s share s a prefx of the other s and thus recover the secret. Usng Elas s prefx code constructons [El75] we get a scheme n whch the share sze s roughly log t + 2 log log t bts. On the other hand, any secret sharng scheme for the evolvng 2-threshold access structure n whch the share sze of the t th party s σ(t), mples the exstence of a prefx code n whch the length of the t th codeword s σ(t). Ths comes from the fact that the share lengths n a 2-threshold scheme must satsfy Kraft s nequalty whch s a suffcent condton to yeld prefx codes. Evolvng 2-threshold, drectly. The scheme that results from the connecton to prefx codes s optmal when sharng a 1-bt secret. Our next step s to consder a drect constructon of a scheme for ths access structure whch has several advantages: (1) t supports sharng long secrets much more effcently, (2) t serves as a warm-up to our constructon for the evolvng k-threshold access structure, and (3) t s lnear (a property that may be mportant n applcatons). We focus on sharng a sngle bt n ths overvew and refer to Secton 4.2 for the general case. The approach of [CT12] for the evolvng 2-threshold access structure s to gve party t a random bt b t and all bts s b 1,..., s b t 1. Ths clearly allows for each par of partes to reconstruct the secret and ensures that for every sngle party the secret remans hdden. The share sze of the t th party n ths scheme s t. (Essentally the same scheme also follows from our general constructon n Secton 3 wth a smple effcency mprovement descrbed towards the end of that secton.) Generalzng ths dea to larger values of k results wth shares of sze roughly t k 1. Whereas the above approach s somewhat nave (and very neffcent n terms of share sze), our constructon s more subtle and results wth exponentally shorter shares. Our man buldng block s a doman reducton technque whch allows us to start wth a nave soluton and apply t only on a small number of partes to get an overall mproved constructon. Detals follow. We assgn each party a generaton, where the g th generaton conssts of 2 g partes (.e. the generatons are of geometrcally ncreasng sze). Wthn each generaton we execute a standard 4

6 secret sharng scheme for 2-threshold. Notce that here we know exactly how many partes are n the same generaton: party t s part of generaton g = log t and the sze of that generaton s sze(g) t. A standard secret sharng scheme for 2-out-of-t costs roughly log t bts (usng Shamr s scheme; see Clam 2.4). Ths solves the case n whch both partes come from the same generaton. To handle the case where the two partes come from dfferent generatons we use a (possbly nave) scheme for the evolvng 2-threshold access structure. For each generaton we generate one share for the evolvng scheme and gve t to each party n that generaton. Thus, f two partes from dfferent generatons come together they hold two dfferent shares for the evolvng scheme that allow them to reconstruct the secret. Snce we generate one share of the evolvng scheme per generaton, party t holds the share of the (g = log t) th party of the evolvng scheme! Summng up, f we start wth a scheme n whch the share sze of the t th party s σ(t), then we end up wth a scheme wth share sze roughly σ (t) = log t + σ(log t). To get our result we start wth a scheme n whch σ(t) = t (descrbed above) and teratvely apply ths argument to get better and better schemes. Evolvng k-threshold. There are several deas underlyng the generalzaton of the 2-threshold scheme to work for any threshold k. As before, we assgn each party to a generaton, but now the g th generaton s roughly of sze 2 (k 1) g. Ths means that party t s n generaton g = (log t)/(k 1) that ncludes sze(g) = t 2 k 1 partes. Agan, wthn a generaton we apply a standard k-out-ofsze(g) secret sharng scheme. Ths costs us log(sze(g)) log t + k bts usng Shamr s scheme. Ths solves the problem f k partes come from the same generaton. We are left wth the case where the k partes come from at least two dfferent generatons. For ths we use a (possbly nave) scheme for the evolvng k-threshold access structure. For each generaton we generate k 1 shares s 1,..., s k 1 for the evolvng scheme and share each s usng a standard -out-of-sze(g) secret sharng scheme. Thus, f w k 1 partes from some generaton come together, they can reconstruct s 1,..., s w whch are w shares for the evolvng scheme. Therefore, any k partes (that come from at least two generatons) can reconstruct k shares for the evolvng k-threshold scheme that enable them to reconstruct the secret. Snce we generate k 1 shares of the evolvng scheme per generaton, party t holds (roughly) the share of the (log t + k) th party of the evolvng scheme. The share sze needed to share each s s max{log(sze(g)), s } max{log t + k, σ(log t + k)} (usng Shamr s scheme; see Clam 2.4). Summng up, f we start wth a scheme n whch the share sze of the t th party s σ(t), then we end up wth a scheme wth share sze roughly σ (t) = log t + (k 1) max{log t + k, σ(log t + k)}. A small optmzaton s that sharng s 1 costs just s 1, as we can gve s 1 to each party (smlarly to what we dd n the k = 2 case). We want to teratvely apply ths doman reducton procedure. For ths we have to specfy the ntal scheme. If we start wth the scheme that results from the constructon n Theorem 1.1 whch has share sze roughly 2 t (or roughly t k wth a mnor optmzaton), then the resultng scheme wll have a factor that depends exponentally on k. Ths makes the scheme mpractcal even for small values of k. To get around ths we present a talor-made constructon for the evolvng k-threshold n whch the share sze of party t has almost lnear dependence on t and k. Specfcally, the share sze n ths scheme s kt log(kt). For ths, we use a varant of the scheme for general access structures such that we group partes nto generatons and use the fact that we only care about the number of partes n each generaton and not ther denttes. We assocate wth each generaton g bts r A for A = (c 0,..., c g ) {0,..., k} g, where each c ndcates how many partes arrve from generaton. For 5

7 each such A the dealer chooses the element r A such that f c g = 0, then r A = 0, f g =0 c < k, the bt r A s chosen unformly at random, and f g =0 c = k, t s set to be r A = r (c0 )... r (c0,...,c g 1 ) s. If c g > 0, the dealer shares r A among the partes n generaton g usng a Shamr c g -out-of-sze(g) scheme, where sze(g) s the number of partes n a generaton. Lettng the number of partes n generaton g be roughly k g+1, we get that the share sze of a party n generaton g s k g+1 log(k g+1 ) (snce we apply Shamr s scheme on each r A ). Party t s part of generaton g = log k t whch mples that ts share sze s kt log(kt), as requred. 2 Model and Defntons For an nteger n N we denote by [n] the set {1,..., n}. We denote by log the base 2 logarthm and assume that log 0 = 0. For a set X we denote by x X the process of samplng a value x from the unform dstrbuton over X We start by brefly recallng the standard settng of (perfect) secret sharng. Let P n = {1,..., n} be a set of n partes. A collecton of subsets A 2 Pn s monotone f for every B A, and B C t holds that C A. Defnton 2.1 (Access structure). An access structure A 2 Pn s a monotone collecton of nonempty subsets. Subsets n A are called qualfed and subsets not n A are called unqualfed. Defnton 2.2 (Threshold access structure). For every n N and 1 k n, let (k, n)-thr be the threshold access structure over n partes whch contans all subsets of sze at least k. A (standard) secret sharng scheme nvolves a dealer who has a secret, a set of n partes, and an access structure A. A secret sharng scheme for A s a method by whch the dealer dstrbutes shares to the partes such that any subset n A can reconstruct the secret from ts shares, whle any subset not n A cannot reveal any nformaton on the secret. More precsely, a secret sharng scheme for an access structure A conssts of a par of probablstc algorthms (SHARE, RECON). SHARE gets as nput a secret s (from a doman of secrets S) and a number n, and generates n shares Π (s) 1,..., Π(s) n. RECON gets as nput the shares of a subset B and outputs a strng. The requrements are: 1. For every secret s S and every qualfed set B A, t holds that Pr[RECON({Π (s) } B, B) = s] = For every unqualfed set B / A and every two dfferent secrets s 1, s 2 S, t holds that the dstrbutons ({Π (s 1) } B ) and ({Π (s 2) } B ) are dentcal. The share sze of a scheme s the maxmum number of bts each party holds n the worst case over all partes and all secrets. A useful fact that we use n some of our proofs s that concatenaton of ndependently generated shares does not harm securty. That s, for any two secrets s 0, s 1 and any two access structures A 0, A 1, f a set of partes s unqualfed n both structures, then t cannot gan any nformaton regardng the secrets. We state ths fact for two access structures and two secrets next, but t naturally generalzes to more. Fact 2.3. Fx two access structures A 0, A 1 wth correspondng secret sharng schemes, four secrets s (0) 0, s(0) 1, s(1) 0, s(1) 1, and a set of partes B / A 1, A 2 that s unqualfed both n A 0 and n A 1. 6

8 Then, the dstrbutons ({Π (s(0) 0 ) 0,, Π (s(0) 1 ) 1, } B ) and ({Π (s(1) 0 ) 0,, Π (s(1) 1 ) 1, } B ) are dentcal, where Π (s(a) c, for a, b {0, 1} and [n] s a random varable for the share of the th party when sharng the secret s (a) b accordng to A b. The well known scheme of Shamr [Sha79] for the (k, n)-thr access structure (based on polynomal nterpolaton) satsfes the followng. Clam 2.4 ([Sha79]). For every n N and 1 k n, there s a secret sharng scheme for secrets of length m and the (k, n)-thr access structure n whch the share sze s l, where l max{m, log q} and q > n s a prme number (or a power of a prme). Moreover, f k = 1 or k = n, then l = m Secret Sharng for Evolvng Access Structures We proceed wth the defnton of an evolvng access structure. Roughly speakng, the partes arrve one by one and, n the most general case, a qualfed subset s revealed only when all partes n that subset are present (n specal cases the access structure s known to begn wth, but there s no upper bound on the number of partes). To make sense of sharng a secret wth respect to such a sequence of access structures, we requre that the changes to the access structure are monotone, namely, partes are only added and qualfed sets reman qualfed. Defnton 2.5 (Evolvng access structure). An evolvng access structures A 2 N s a (possbly nfnte) monotone collecton of subsets of the natural numbers such that for any t N, the collecton of subsets A t A [t] s an access structure. Ths defnton naturally gves rse to an evolvng varant of threshold access structures (see Defnton 2.2). Here, we thnk of k as fxed, namely, ndependent of the number of partes. Defnton 2.6 (Evolvng threshold access structure). For every k N, let evolvng k-thr be the evolvng threshold access structure whch contans all subsets of 2 N of sze at least k. We generalze the defnton of a standard secret sharng scheme to apply for evolvng access structures. Intutvely, n ths settng, at any pont t N n tme, there s an access structure A t whch defnes the qualfes and unqualfed subsets of partes. Defnton 2.7 (Secret sharng for evolvng access structures). Let A be an evolvng access structure. Let S be a doman of secrets, where S 2. A secret sharng scheme for A and S conssts of a par of algorthms (SHARE, RECON). The sharng procedure SHARE and the reconstructon procedure RECON satsfy the followng requrements: 1. SHARE(s, {Π (s) 1,..., Π(s) t 1 }) gets as nput a secret s S and the secret shares of partes 1,..., t 1. It outputs a share for the t th party. For t N and secret shares Π (s) 1,..., Π(s) t 1 generated for partes {1,..., t 1}, respectvely, we let be the secret share of party t. Π (s) t SHARE(s, {Π (s) 1,..., Π(s) t 1 }) We abuse notaton and sometmes denote by Π (s) t the secret share of party t generated as above. b ) the random varable that corresponds to 3 Schemes n whch the share sze s equal to the secret sze are known as deal secret sharng schemes. 7

9 2. Correctness: For every secret s S and every t N, every qualfed subset n A t can reconstruct the secret. That s, for s S, t N, and B A t, t holds that [ ] Pr RECON({Π (s) } B, B) = s = 1, where the probablty s over the randomness of the sharng and reconstructon procedures. 3. Secrecy: For every t N, every unqualfed subset B / A t, and every two secret s 1, s 2 S, the dstrbuton of the secret shares of partes n B generated wth secret s 1 and the dstrbuton of the shares of partes n B generated wth secret s 2 are dentcal. Namely, the dstrbutons ({Π (s 1) } B ) and ({Π (s 2) } B ) are dentcal. The share sze of the t th party n a scheme for an evolvng access structure s max Π t, namely the number of bts party t holds n the worst case over all secrets and prevous assgnments. 4 On choosng the access structure adaptvely. One can also consder a stronger defnton n whch A t s chosen at tme t (rather than ahead of tme) as long as qualfed sets reman qualfed. In ths varant, the RECON procedure gets the addtonal new qualfed sets as an addtonal parameter. Our constructon of a secret sharng scheme for general evolvng access structures n Secton 3 works for ths noton as well. On the doman of secrets. Unless otherwse stated, we usually assume that the secret s a sngle bt (ether 0 or 1). One can generalze any such scheme to support longer secrets by secret sharng every bt of the secret ndependently, sufferng a multplcatve factor n share sze that depends on the length of the secret. When we generalze our schemes to support long secrets, ths nave generalzaton wll be our benchmark. 2.2 Warm-Up: Undrected st-connectvty We start wth a smple warm-up scheme. We show that the standard scheme for the st-connectvty access structure can be easly adapted to the evolvng settng. In ths access structure partes correspond to edges of an undrected graph G = (V, E). There are two fxed vertces n the graph called s and t (where s, t V ). A set of partes (.e. edges) s qualfed f and only f they nclude a path from s to t. Around 1989 Benaloh and Rudch [BR88] constructed a (standard) secret sharng for ths access structure. The dealer, gven a secret s {0, 1}, assgns wth each vertex v V a label. For v = s the label s w s = s, for v = t the label s w t = 0 and for the rest of the vertces the label s chosen ndependently unformly at random w v {0, 1}. The share of a party e = (u, v) E s w u w v. For correctness consder a set of partes that nclude a path s = v 1 v 2... v k = t from s to t. To reconstruct the secret, the partes XOR ther shares to get (w v1 w v2 ) (w v2 w v3 ) (w vk 1 w vk ) = w v1 w vk = s. For securty, one can show that any subset of partes that do not form a path from s to t, hold shares whch are ndependent of the secret. One way to prove ths s to show that the number of 4 Ths means that the share sze s bounded, whch s almost always the case. An excepton s the scheme (for ratonal secret sharng) of Kol and Naor [KN08] n whch the share sze does not have a fxed upper bound. 8

10 ways to get to the shares of the partes gven the secret 0 s equal to the number of ways to get to these shares gven the secret 1 (see [Be11, 3.2]). One can observe that ths access structure and scheme naturally generalze to the evolvng settng. In ths settng, we consder an evolvng (possbly nfnte) graph, where the set of nodes and edges are unbounded. At any pont n tme an arbtrary set of vertces and edges can be added to the graph. An addton of an edge corresponds to a new party added to the scheme. The specal vertces s and t are fxed ahead of tme and cannot change (ths s to ensure the access structure s evolvng). Intally, the dealer assgns labels for the specal vertces s and t, as before (.e. t sets w s = s and w t = 0). For the rest of the vertces the dealer assgns (unformly random) labels only on demand: When a new edge e = (u, v) s added to the graph (whch corresponds to a new party), the dealer gves the party correspondng to the edge e the XOR of the labels of the vertces u and v. Correctness and securty of ths scheme follow smlarly to the correctness and securty of the standard scheme. One can see that the share sze of each party s exactly the sze of the secret. 3 A Scheme for General Evolvng Access Structures We gve a constructon of a secret sharng scheme for every evolvng access structure. Theorem 3.1 (Theorem 1.1 restated). For every evolvng access structure there s a secret sharng scheme for a 1-bt secret, where for any t N, the share sze of the t th party s at most 2 t 1. Proof of Theorem 3.1. Let A be an evolvng access structure. Let s {0, 1} be the secret to be shared. We descrbe what the dealer stores and how t prepares a share for an arrvng party. At tme t the dealer mantans 2 t bts denoted by s A for all A [t]. Intally, we set s = s. The value of each s A for A = { 1,..., k, t} [t] s defned as follows: 1. If A / A t, the dealer samples r A {0, 1} unformly at random and sets s A = s A\{t} r A. The dealer gves party t the bt r A. 2. If A A t and A \ {t} / A t 1, the dealer gves party t the bt s A\{t}. Party t holds at most a sngle bt for every A [t] such that t A. Ths mples that the share sze of party t s at most 2 t 1 bts. Correctness and securty. We argue correctness at tme t N, where the access structure s A t. Let A = { 1,..., k, t} be a mnmal qualfed set of partes present at tme t. Snce A A t, for every j [k], party j holds the bt r {1,..., j }. Party t, by constructon, holds the bt s A = r {1 }... r {1,..., k } s. Therefore, by XOR-ng all the shares the partes n A can recover s. We prove that our scheme s secure by nducton on t. For t = 1, the scheme s secure snce ether the set consstng of frst party s qualfed, n whch case the scheme s secure (snce no nformaton s publc); f the frst party s unqualfed by tself, then the ths party smply gets a random bt r {1} whch s ndependent of the secret. Suppose that the scheme s secure for access structures over t 1 partes. Consder a set A = { 1,..., k, t} of partes that form an unqualfed set relatve to A t. Consder the dealer rght after t performs the sharng to the frst party. At ths pont the dealer mantans two bts: s and s {1}. Now, we observe that the remanng procedure of the dealer conssts of two nstances of the scheme for two access structures over t 1 partes and two secrets. 9

11 The key pont s that these secret-sharngs are done ndependently (and hence ther concatenaton s secure). Frst, the dealer shares the secret s among partes 2,..., t w.r.t the access structure A 0 t = {A {2,..., t} A A t } that contans all qualfed sets n whch the frst party does not partcpate n. Second, the dealer shares the secret s {1} among partes 2,..., t w.r.t the access structure A 1 t = {A {2,..., t} {1} A A t } that contans all qualfed sets n whch the frst party s a member. Snce the orgnal set of partes s unqualfed, t must be that (1) the remanng set of partes (.e. A \ {1}) s unqualfed n A 0 t and (2) ether 1 / A or the remanng set of partes s unqualfed n A 1 t. We can apply the nducton hypothess on the sharng accordng to A 0 t and get that the secret s ndependent of the correspondng shares. For the second part, there are two cases: (1) If 1 / A, then the shared secret (s {1} ) s ndependent of s and therefore the whole vew of the shares of A s ndependent of s. (2) If 1 A but A / A t, then the set A knows the maskng of s whch s a random bt r {1}, however, the nducton hypothess ensures that the shares of the remanng partes are ndependent of s {1} = s r {1}, whch mples that they are ndependent of the secret. Each case s ndependent of one another and n each case the resultng shares are ndependent of the secret. Usng Fact 2.3, we conclude that the secret remans perfectly hdden. The share sze n some specal cases. In some cases, dependng on the access structure, t s possble to analyze the worst case share sze more carefully. Recall that n our scheme each party gets two types of bts: 1. A bt for each unqualfed subset of [t] that party t partcpates n. For the case when the access structure s known ahead of tme, the only unqualfed sets to consder are those that can be expanded to a qualfed subset usng future partes. 2. A bt for each qualfed subset of [t] that party t completes (.e. t s the last one). Thus, when the number of unqualfed sets s small, we can get a better bound. Consder, for example, the evolvng 2-thr access structure. At tme t, there are only t unqualfed sets (the sngletons). Thus, the share sze of the t th party s exactly t (we use ths fact n Secton 4). More generally, for the evolvng k-thr access structure, there are k 2 ( t 1 ) ( =0 unqualfed sets and t 1 ) k 1 qualfed sets whch t completes, mplyng a scheme wth share sze roughly t k 1. 4 Evolvng 2-Threshold In ths secton we present an equvalence between prefx codes for the ntegers and secret sharng schemes for 1-bt secrets and the evolvng 2-threshold access structure. Ths constructon gves an effcent scheme (based on Elas s prefx code [El75]) whch we show to be optmal n terms of share sze. Theorem 4.1 (Theorem 1.3 restated). Let σ : N N. A prefx code for the ntegers n whch the length of the t th codeword s σ(t) exsts f and only f a secret sharng scheme for the evolvng 2-threshold access structure and a 1-bt secret n whch the share sze of the t th party s σ(t). Corollary 4.2. There s a secret sharng scheme for the evolvng 2-thr access structure and a 1-bt secret n whch the share sze of the t th party s log t + 2 log log t

12 Corollary 4.3. For any constants c, l N, there s no secret sharng scheme for the evolvng 2- thr access structure n whch for every t N the share sze of the t th party s at most log t + log log t log (l) t + c, where log () (t) s the -tmes repeated log of t. Furthermore, we gve a drect constructon of a secret sharng scheme the evolvng 2-threshold access structure that s more effcent when sharng longer secrets. Ths scheme has the addtonal advantages that t serves as a warm-up for our scheme for the evolvng k-thr access structure gven n Secton 5 and t s lnear (whereas the scheme from Corollary 4.2 s non-lnear). Theorem 4.4. There s a secret sharng scheme for the evolvng 2-thr access structure and an l-bt secret n whch the share sze of the t th party s log t + (l + 1) log log t + 4l The Connecton to Prefx Codes Here we prove Theorem 4.1, the tght connecton between schemes for the evolvng 2-threshold access structure and prefx codes. We also deduce Corollares 4.2 and 4.3, upper and lower bounds (respectvely) on share sze n schemes for the evolvng 2-threshold access structure. Proof of the only f part of Theorem 4.1. Let Σ: N {0, 1} be a prefx code for the ntegers. That s, for any t 1, t 2 N such that t 1 t 2, t holds that Σ(t 1 ) s not a prefx of Σ(t 2 ). For t N denote by σ(t) the length of the codeword Σ(t). The scheme. Let s {0, 1} be the secret to be shared. Let w be an nfnte random bnary strng. The dealer generates the strng as needed: at tme t N the dealer holds the prefx of length σ(t) of the strng w, denoted by w [σ(t)] (for smplcty we assume that σ(t) s monotoncally ncreasng, but ths s not necessary). The share of party t s a strng u t such that: 1. If s = 0, then u t = w [σ(t)]. 2. If s = 1, then u t = Σ(t) w [σ(t)] (bt-wse XOR). The share sze of the t th player n ths scheme s σ(t). To reconstruct the secret, any two dfferent partes t 1 and t 2, holdng shares u 1 and u 2, respectvely, where u 1 u 2, should check f u 1 s a prefx of u 2. If t s a prefx, then they output s = 0 and otherwse, they output s = 1. Correctness and securty. If s = 0, then snce u 1 and u 2 are both prefxes of the same strng w t holds that u 1 s a prefx of u 2. On the other hand, f s = 1 then u 1 = Σ(t 1 ) w [σ(t1 )] and u 2 = Σ(t 2 ) w [σ(t2 )], where w [σ(t1 )] s a prefx of w [σ(t2 )]. Snce Σ s a prefx code, Σ(t 1 ) s not a prefx of Σ(t 2 ), and thus u 1 s not a prefx of u 2. Securty follows snce for both s = 0 and for s = 1 each sngle party t holds a sngle strng u t whch s unformly dstrbuted over {0, 1} σ(t). In case s = 0 ths s true by constructon, and n case s = 1 ths s true snce all the party sees s the codeword Σ(t) XORed wth w [σ(t)] whch s unform. Proof of Corollary 4.2 (an nstantaton va Elas s code). We descrbe the prefx code of Elas [El75] as a two step soluton (for smplcty, n the descrpton we gnore roundng ssues). The frst step wll be a basc scheme and the second step wll recursvely use the basc scheme to mprove the sze of the encodng (ths s very smlar to the strategy we use n the proof of 11

13 Corollary 4.2). In the basc scheme, a number t N s encoded n two parts: the frst part s log t zeros (ths s just a unary representaton of the length of t) and the second part s the bnary representaton of t. In total, the sze of the encodng s 2 log t + 1. In the second step, we encode the frst part of the basc scheme recursvely usng the basc scheme tself. Specfcally, we encode the unary representaton of the length of t usng 2 log log t + 1 bts va the basc scheme. Ths results wth an encodng of sze log t + 2 log log t + 2 (one can recursvely apply ths transformaton and get an even shorter encodng). Proof of the f part of Theorem 4.1. Assume that we are gven a secret sharng scheme for the evolvng 2-threshold access structure and a 1-bt secret n whch the share sze of the t th party s σ(t). A property that the functon σ( ) must satsfy s mplct n the lower bound of Klan and Nsan [KN90] (that appears n [CCX13, Appendx A]) on the share sze n 2-out-of-n secret sharng schemes. It says that the share szes n such a scheme must satsfy Kraft s nequalty (see below). Clam 4.5 (Implct n [KN90] and [CCX13, Appendx A]). For any n N, n any secret sharng scheme for (2, n)-thr, t holds that n 1 t=1 1, where σ(t) s the share sze of the t th party. 2 σ(t) It s known that Kraft s nequalty gves a necessary and suffcent condton for the exstence of a prefx code for a gven set of codeword lengths. Clam 4.6 ([CT06, Theorem 5.2.1]). For any prefx code over the alphabet {0, 1}, the codeword lengths σ(1),..., σ(n) must satsfy n 1 t=1 1. Conversely, gven a set of codeword lengths that 2 σ(t) satsfy ths nequalty, there exsts a prefx code wth these word lengths. The proof of the suffcent drecton s constructve: gven the collecton of lengths of codewords t s possble to construct the code. Furthermore, we do not need to know the collecton of lengths n advance,.e. we can create the code on the fly, as long as the demand ( 1 t, where σ(t) s 2 σ(t) the sze of the encodng of the number t) does not exceed 1 [CT06, Theorem 5.2.2]. Thus, any secret sharng scheme for the evolvng 2-thr access structure n whch the share sze of the t th party s σ(t), mples the exstence of a prefx code n whch the length of the t th codeword s σ(t) (however, effcency mght not be preserved). Proof of Corollary 4.3 (the lower bound). Assume (towards contradcton) that there s a secret sharng scheme for the evolvng 2-thr access structure n whch the share sze of the t th party s at most σ(t) = log t + log log t log (l) t + c for constants c, l N. We can use ths scheme to mplement a standard secret sharng scheme for (2, n)-thr n whch the share sze of party t [n] s σ(t). By Clam 4.5, we get that 1 n t=1 1 2 σ(t) 1 2 c n t=1 1 t log t... log (l 1) t. But, as n the rght hand sde s at least 1 2 c 1 1 dt = l 1 =0 log() (t) log(l) t, whch s strctly larger than 1 for large enough t. Ths s a contradcton whch proves our clam. 4.2 A Drect Constructon Here we prove Theorem 4.4, a drect constructon of a secret sharng scheme for the evolvng 2-thr access structure. The constructon s based on the followng recursve composton lemma. 12

14 Lemma 4.7. Assume that there exsts a secret sharng scheme for the evolvng 2-thr access structure and an l-bt secret n whch the share sze of the t th party s σ(t). Then, there exsts a secret sharng scheme for the evolvng 2-thr access structure n whch the share sze of the t th party s max{l, log t} + σ(log t + 1). Proof. Let Π be a constructon of a secret sharng scheme for evolvng 2-thr n whch the share sze of the t th party when sharng an l-bt secret s σ(t). Let s {0, 1} l be the secret to be shared. Each party, when t arrves, s assgned to a generaton. The generatons are growng n sze: For g = 0, 1, 2... the g th generaton begns when the (2 g )-th party arrves. Therefore, the sze of the g th generaton, namely, the number of partes that are part of ths generaton, s sze(g) = 2 g and party t N s part of generaton g = log t. When a generaton begns the dealer prepares shares for all partes that are part of that generaton. Let us focus on the begnnng of the g th generaton and descrbe the dealer s procedure: 1. Splt s usng a secret sharng scheme for (2, sze(g))-thr. Denote the resultng shares by u (g) 1,..., u(g) sze(g). 2. Generate one share usng the secret sharng scheme Π gven the secret s and prevous shares {v () } {0,...,g 1}. Denote the resultng share by v (g). 3. Set the secret share of the j th party n the g th generaton (.e. j [sze(g)]) to be ( u (g) j, v (g)). The output of the scheme s depcted n Fgure 1. 1 v (0) Generaton 0 u 1 (0) 2 v (1) 3 v (1) Generaton 1 u 1 (1) u 2 (1) 4 v (2) 5 v (2) 6 v (2) 7 v (2) Generaton 2 u 1 (2) u 2 (2) u 3 (2) u 4 (2) 8 v (3) 9 v (3) 10 v (3) 11 v (3) 12 v (3) 13 v (3) 14 v (3) 15 v (3) Generaton 3 u 1 (3) u 2 (3) u 3 (3) u 4 (3) u 5 (3) u 6 (3) u 7 (3) u 8 (3) Fgure 1: The shares of partes 1,..., 15 from generatons 0,..., 3. 13

15 Correctness and securty. Let t 1, t 2 N be any two dfferent partes. If t 1 and t 2 are from the same generaton g (.e. f g = log t 1 = log t 2 ), then they can reconstruct the secret s usng the reconstructon procedure of the (2, sze(g))-thr scheme usng the correspondng u (g) shares. If they are from dfferent generatons g 1 g 2, then the partes can compute s usng the reconstructon procedure of the evolvng 2-thr scheme and the two shares v (g1) and v (g2). For securty consder any sngle party t N from generaton g. We use Fact 2.3 and the assumptons that the schemes (2, sze(g))-thr and evolvng 2-thr are secure, together wth the fact that the sharng accordng to both of them s done ndependently, to get that the secret s perfectly hdden and conclude the proof. Share sze analyss. Fx party t N and assume that t s the j th party of generaton g = log t. Its share s composed of two parts: 1. u (g) j generated by secret sharng s usng a scheme for (2, sze(g))-thr. Snce sze(g) = 2 g and usng Clam 2.4 we get that u (g) j max{l, log (sze(g))} max{l, log t}. 2. v (g) generated by generatng one share of a secret sharng scheme Π for evolvng 2-thr. Recall that g shares were generated for prevous generatons. Therefore, v (g) = σ(g + 1) = σ( log t + 1). Thus, the total share sze n the scheme Π s bounded by max{l, log t} + σ(log t + 1). Proof of Theorem 4.4. We start wth the scheme descrbed n the end of Secton 3 for sharng a 1-bt secret for the evolvng 2-threshold access structure. We denote by Π (0) ths scheme when used to share an l-bt secret (by sharng each bt ndependently). The share sze n ths scheme s 5 σ (0) (t) = lt. Usng Lemma 4.7 we get a scheme Π (1) n whch the share sze of the t th party s σ (1) (t) = max{l, log t} + σ (0) (log t + 1) = max{l, log t} + l log t + l (l + 1) log t + 2l. Applyng Lemma 4.7 agan we get a scheme Π (2) wth shares of sze σ (2) (t) = max{l, log t} + σ (1) (log t + 1) max{l, log t} + (l + 1) log(log t + 1) + 2l log t + (l + 1) log log t + 4l + 1. We note that by applyng Lemma 4.7 more tmes one can push the multplcatve dependence on l to even lower-order terms. 5 Alternatvely, we can use the constructon of [CT12] (see Secton 1.3) whch gves the same share sze. 14

16 5 Evolvng k-threshold In ths secton we gve a constructon of a secret sharng scheme for the evolvng k-threshold access structure for general k N. The scheme that we gve s lnear. Theorem 5.1 (Theorem 1.2 restated). For every k, l N, there s a secret sharng scheme for the evolvng k-thr access structure and an l-bt secret n whch for every t N the share sze of the t th party s (k 1) log t + 6k 4 l log log t log log log t + 7k 4 l log k. Our approach follows the general blueprnt of the one from Theorem 4.4. We start wth a basc scheme that has good dependency on k but poor dependency on t, and use a doman reducton technque n order to obtan better dependency on t. The queston s whch basc scheme should we use. If we start wth the scheme for the evolvng k-threshold access structure n whch the share sze s exponental n t or k (whch s what we get usng the scheme from Theorem 3.1), then (after the recursve composton) the share sze wll depend exponentally on k. To overcome ths, n Secton 5.1, we present a talor-made constructon for the evolvng k- threshold access structure n whch the share sze of party t has almost lnear dependence on k t. The recursve composton lemma appears n Secton 5.2 and the proof of Theorem 5.1 appears n Secton The Basc Scheme The man result of ths subsecton s a constructon of a secret sharng scheme for the evolvng k- thr access structure n whch the share sze of party t s roughly lnear n k t. Lemma 5.2. For every k, l N, there s a secret sharng scheme for the evolvng k-thr access structure and an l-bt secret n whch for every t N the share sze of the t th party s bounded by kt max{l, log(kt)}. Proof. Our scheme can be vewed as a varant of the scheme for general access structures from Secton 3 wth an addtonal mechansm of generatons. Our man dea s not to consder all possble combnatons of k partes (as n Secton 3), but to group partes nto generatons, gnore the denttes of the partes wthn a generaton, and only focus on ther quantty. Each party, when t arrves, s assgned to a generaton. Party t N s assgned to generaton g = log k t. The generatons are growng n sze: For g = 0, 1, 2... the g th generaton begns when the k g -th party arrves. Therefore, the sze of the g th generaton (.e. the number of partes that are members of ths generaton), s sze(g) = k g+1 k g = (k 1) k g. Let s {0, 1} l be the secret. When a generaton g begns the dealer remembers k g l-bt strngs s A for all A = (c 0,..., c g 1 ) {0,..., k} g (where f g = 0 t remembers only the secret). Intutvely, each such s A s an l-bt strng that we share to the partes n generaton g assumng that n generaton {0,..., g 1} c partes arrved. We explan how the dealer sets the value of s A for A = (c 0,..., c g ). Notaton: let s prev(a) = s f g = 0 and s prev(a) = s (c0,...,c g 1 ) otherwse. 1. If c g = 0, set s A = s prev(a) and HALT. 2. If c c g < k, then the dealer: 15

17 (a) samples r A {0, 1} l unformly at random. (b) sets s A = s prev(a) r A. (c) shares the l-bts r A among the partes n the g th generaton usng Shamr s (c g, sze(g))-thr secret sharng scheme. 3. If c c g = k, then the dealer shares the l-bt strng s prev(a) among the partes n the g th generaton usng Shamr s (c g, sze(g))-thr secret sharng scheme. For correctness, consder any mnmal qualfed set of partes, namely a set of k partes. Let g be the generaton of the latest party and let A = (c 0,..., c g ) be a tuple of numbers that ndcates how many partes arrve from each generaton. It holds that g =0 c = k and wthout loss of generalty c g > 0. We show that these partes can recover s, as requred. Indeed, the c g partes from generaton g can recover s prev(a) whch s equal to s r (c0 ) r (c0,c 1 )... r (c0,...,c g 1 ) (or t s exactly s f g = 0 n whch case we are done). Now, the c 0 partes from generaton 0 can recover r (c0 ), the c 1 partes from generaton 1 can recover r (c0,c 1 ), and so on. Thus, all present partes together can recover s, as requred. The proof of securty s by nducton, n the sprt of the proof of securty of the scheme for general access structures gven n Secton 3. We assume (by nducton) that the scheme s secure for g generatons and all thresholds up to k and prove that the scheme s secure for g + 1 generatons and all thresholds up to k. The base case follows mmedately from the securty of Shamr s scheme. After the dealer generates shares for generaton 0, t holds k l-bt strng: s (0), s (1),..., s (k 1), where s (0) = s s the orgnal secret and where for [k 1] s () = s r () for a random r (). Namely, each such s () s an ndependent one-tme pad of the secret, where each pad s shared among the partes n the 0 th generaton such that any of them can recover t (usng Shamr s scheme). The remanng procedure of the dealer conssts of ndependently sharng each such s () va the evolvng (k )-thr access structure among g generatons. Snce the set controlled by the adversary s unqualfed, for each such secret s (), ether the padded secret or the pad cannot be recovered, ether by the securty of Shamr s scheme or by the securty of the scheme guaranteed by the nducton hypothess, respectvely. Thus, snce the sharng among dfferent secrets s done ndependently, we conclude by Fact 2.3 that the secret s perfectly hdden gven the whole set of shares seen by the adversary. Share sze analyss. The share of party t from generaton g s composed of k g+1 shares generated va standard threshold schemes over sze(g) partes. Thus, n total, the share sze of party t s bounded by k g+1 max{l, log(sze(g))}. Recall that g = log k t and sze(g) = (k 1) k g. Therefore, the share sze s bounded by k t max{l, log((k 1) t)} kt max{l, log(kt)}. 5.2 Recursve Composton Lemma 5.3. Fx k, l N. Assume that there exsts a secret sharng scheme for the evolvng k- thr access structure and an l-bt secret n whch the share sze of the t th party s σ(t). Then, there exsts a secret sharng scheme for the evolvng k-thr access structure and an l-bt secret n whch the share sze of the t th party s at most (k 1) log t + k σ(log t + k) + k 2 + l. 16

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

COS 511: Theoretical Machine Learning. Lecturer: Rob Schapire Lecture #21 Scribe: Lawrence Diao April 23, 2013

COS 511: Theoretical Machine Learning. Lecturer: Rob Schapire Lecture #21 Scribe: Lawrence Diao April 23, 2013 COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #21 Scrbe: Lawrence Dao Aprl 23, 2013 1 On-Lne Log Loss To recap the end of the last lecture, we have the followng on-lne problem wth N

More information

Parallel Prefix addition

Parallel Prefix addition Marcelo Kryger Sudent ID 015629850 Parallel Prefx addton The parallel prefx adder presented next, performs the addton of two bnary numbers n tme of complexty O(log n) and lnear cost O(n). Lets notce the

More information

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002

TCOM501 Networking: Theory & Fundamentals Final Examination Professor Yannis A. Korilis April 26, 2002 TO5 Networng: Theory & undamentals nal xamnaton Professor Yanns. orls prl, Problem [ ponts]: onsder a rng networ wth nodes,,,. In ths networ, a customer that completes servce at node exts the networ wth

More information

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem.

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem. Topcs on the Border of Economcs and Computaton December 11, 2005 Lecturer: Noam Nsan Lecture 7 Scrbe: Yoram Bachrach 1 Nash s Theorem We begn by provng Nash s Theorem about the exstance of a mxed strategy

More information

Applications of Myerson s Lemma

Applications of Myerson s Lemma Applcatons of Myerson s Lemma Professor Greenwald 28-2-7 We apply Myerson s lemma to solve the sngle-good aucton, and the generalzaton n whch there are k dentcal copes of the good. Our objectve s welfare

More information

CS 286r: Matching and Market Design Lecture 2 Combinatorial Markets, Walrasian Equilibrium, Tâtonnement

CS 286r: Matching and Market Design Lecture 2 Combinatorial Markets, Walrasian Equilibrium, Tâtonnement CS 286r: Matchng and Market Desgn Lecture 2 Combnatoral Markets, Walrasan Equlbrum, Tâtonnement Matchng and Money Recall: Last tme we descrbed the Hungaran Method for computng a maxmumweght bpartte matchng.

More information

Finance 402: Problem Set 1 Solutions

Finance 402: Problem Set 1 Solutions Fnance 402: Problem Set 1 Solutons Note: Where approprate, the fnal answer for each problem s gven n bold talcs for those not nterested n the dscusson of the soluton. 1. The annual coupon rate s 6%. A

More information

Elements of Economic Analysis II Lecture VI: Industry Supply

Elements of Economic Analysis II Lecture VI: Industry Supply Elements of Economc Analyss II Lecture VI: Industry Supply Ka Hao Yang 10/12/2017 In the prevous lecture, we analyzed the frm s supply decson usng a set of smple graphcal analyses. In fact, the dscusson

More information

Tests for Two Correlations

Tests for Two Correlations PASS Sample Sze Software Chapter 805 Tests for Two Correlatons Introducton The correlaton coeffcent (or correlaton), ρ, s a popular parameter for descrbng the strength of the assocaton between two varables.

More information

Dr. A. Sudhakaraiah* V. Rama Latha E.Gnana Deepika

Dr. A. Sudhakaraiah* V. Rama Latha E.Gnana Deepika Internatonal Journal Of Scentfc & Engneerng Research, Volume, Issue 6, June-0 ISSN - Splt Domnatng Set of an Interval Graph Usng an Algorthm. Dr. A. Sudhakaraah* V. Rama Latha E.Gnana Deepka Abstract :

More information

Quiz on Deterministic part of course October 22, 2002

Quiz on Deterministic part of course October 22, 2002 Engneerng ystems Analyss for Desgn Quz on Determnstc part of course October 22, 2002 Ths s a closed book exercse. You may use calculators Grade Tables There are 90 ponts possble for the regular test, or

More information

Random Variables. b 2.

Random Variables. b 2. Random Varables Generally the object of an nvestgators nterest s not necessarly the acton n the sample space but rather some functon of t. Techncally a real valued functon or mappng whose doman s the sample

More information

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic Appendx for Solvng Asset Prcng Models when the Prce-Dvdend Functon s Analytc Ovdu L. Caln Yu Chen Thomas F. Cosmano and Alex A. Hmonas January 3, 5 Ths appendx provdes proofs of some results stated n our

More information

Financial mathematics

Financial mathematics Fnancal mathematcs Jean-Luc Bouchot jean-luc.bouchot@drexel.edu February 19, 2013 Warnng Ths s a work n progress. I can not ensure t to be mstake free at the moment. It s also lackng some nformaton. But

More information

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode.

Measures of Spread IQR and Deviation. For exam X, calculate the mean, median and mode. For exam Y, calculate the mean, median and mode. Part 4 Measures of Spread IQR and Devaton In Part we learned how the three measures of center offer dfferent ways of provdng us wth a sngle representatve value for a data set. However, consder the followng

More information

Tests for Two Ordered Categorical Variables

Tests for Two Ordered Categorical Variables Chapter 253 Tests for Two Ordered Categorcal Varables Introducton Ths module computes power and sample sze for tests of ordered categorcal data such as Lkert scale data. Assumng proportonal odds, such

More information

MgtOp 215 Chapter 13 Dr. Ahn

MgtOp 215 Chapter 13 Dr. Ahn MgtOp 5 Chapter 3 Dr Ahn Consder two random varables X and Y wth,,, In order to study the relatonshp between the two random varables, we need a numercal measure that descrbes the relatonshp The covarance

More information

Equilibrium in Prediction Markets with Buyers and Sellers

Equilibrium in Prediction Markets with Buyers and Sellers Equlbrum n Predcton Markets wth Buyers and Sellers Shpra Agrawal Nmrod Megddo Benamn Armbruster Abstract Predcton markets wth buyers and sellers of contracts on multple outcomes are shown to have unque

More information

Linear Combinations of Random Variables and Sampling (100 points)

Linear Combinations of Random Variables and Sampling (100 points) Economcs 30330: Statstcs for Economcs Problem Set 6 Unversty of Notre Dame Instructor: Julo Garín Sprng 2012 Lnear Combnatons of Random Varables and Samplng 100 ponts 1. Four-part problem. Go get some

More information

Problem Set 6 Finance 1,

Problem Set 6 Finance 1, Carnege Mellon Unversty Graduate School of Industral Admnstraton Chrs Telmer Wnter 2006 Problem Set 6 Fnance, 47-720. (representatve agent constructon) Consder the followng two-perod, two-agent economy.

More information

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME Vesna Radonć Đogatovć, Valentna Radočć Unversty of Belgrade Faculty of Transport and Traffc Engneerng Belgrade, Serba

More information

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost Tamkang Journal of Scence and Engneerng, Vol. 9, No 1, pp. 19 23 (2006) 19 Economc Desgn of Short-Run CSP-1 Plan Under Lnear Inspecton Cost Chung-Ho Chen 1 * and Chao-Yu Chou 2 1 Department of Industral

More information

Topics on the Border of Economics and Computation November 6, Lecture 2

Topics on the Border of Economics and Computation November 6, Lecture 2 Topcs on the Border of Economcs and Computaton November 6, 2005 Lecturer: Noam Nsan Lecture 2 Scrbe: Arel Procacca 1 Introducton Last week we dscussed the bascs of zero-sum games n strategc form. We characterzed

More information

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena Producton and Supply Chan Management Logstcs Paolo Dett Department of Informaton Engeneerng and Mathematcal Scences Unversty of Sena Convergence and complexty of the algorthm Convergence of the algorthm

More information

2.1 Rademacher Calculus... 3

2.1 Rademacher Calculus... 3 COS 598E: Unsupervsed Learnng Week 2 Lecturer: Elad Hazan Scrbe: Kran Vodrahall Contents 1 Introducton 1 2 Non-generatve pproach 1 2.1 Rademacher Calculus............................... 3 3 Spectral utoencoders

More information

Deterministic rendezvous, treasure hunts and strongly universal exploration sequences

Deterministic rendezvous, treasure hunts and strongly universal exploration sequences Determnstc rendezvous, treasure hunts and strongly unversal exploraton sequences Amnon Ta-Shma Ur Zwck Abstract We obtan several mproved solutons for the determnstc rendezvous problem n general undrected

More information

OPERATIONS RESEARCH. Game Theory

OPERATIONS RESEARCH. Game Theory OPERATIONS RESEARCH Chapter 2 Game Theory Prof. Bbhas C. Gr Department of Mathematcs Jadavpur Unversty Kolkata, Inda Emal: bcgr.umath@gmal.com 1.0 Introducton Game theory was developed for decson makng

More information

Understanding Annuities. Some Algebraic Terminology.

Understanding Annuities. Some Algebraic Terminology. Understandng Annutes Ma 162 Sprng 2010 Ma 162 Sprng 2010 March 22, 2010 Some Algebrac Termnology We recall some terms and calculatons from elementary algebra A fnte sequence of numbers s a functon of natural

More information

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers II. Random Varables Random varables operate n much the same way as the outcomes or events n some arbtrary sample space the dstncton s that random varables are smply outcomes that are represented numercally.

More information

Creating a zero coupon curve by bootstrapping with cubic splines.

Creating a zero coupon curve by bootstrapping with cubic splines. MMA 708 Analytcal Fnance II Creatng a zero coupon curve by bootstrappng wth cubc splnes. erg Gryshkevych Professor: Jan R. M. Röman 0.2.200 Dvson of Appled Mathematcs chool of Educaton, Culture and Communcaton

More information

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics Unversty of Illnos Fall 08 ECE 586GT: Problem Set : Problems and Solutons Unqueness of Nash equlbra, zero sum games, evolutonary dynamcs Due: Tuesday, Sept. 5, at begnnng of class Readng: Course notes,

More information

Appendix - Normally Distributed Admissible Choices are Optimal

Appendix - Normally Distributed Admissible Choices are Optimal Appendx - Normally Dstrbuted Admssble Choces are Optmal James N. Bodurtha, Jr. McDonough School of Busness Georgetown Unversty and Q Shen Stafford Partners Aprl 994 latest revson September 00 Abstract

More information

arxiv: v1 [math.nt] 29 Oct 2015

arxiv: v1 [math.nt] 29 Oct 2015 A DIGITAL BINOMIAL THEOREM FOR SHEFFER SEQUENCES TOUFIK MANSOUR AND HIEU D. NGUYEN arxv:1510.08529v1 [math.nt] 29 Oct 2015 Abstract. We extend the dgtal bnomal theorem to Sheffer polynomal sequences by

More information

Single-Item Auctions. CS 234r: Markets for Networks and Crowds Lecture 4 Auctions, Mechanisms, and Welfare Maximization

Single-Item Auctions. CS 234r: Markets for Networks and Crowds Lecture 4 Auctions, Mechanisms, and Welfare Maximization CS 234r: Markets for Networks and Crowds Lecture 4 Auctons, Mechansms, and Welfare Maxmzaton Sngle-Item Auctons Suppose we have one or more tems to sell and a pool of potental buyers. How should we decde

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9 Elton, Gruber, Brown, and Goetzmann Modern Portfolo Theory and Investment Analyss, 7th Edton Solutons to Text Problems: Chapter 9 Chapter 9: Problem In the table below, gven that the rskless rate equals

More information

Generation of Well-Formed Parenthesis Strings in Constant Worst-Case Time

Generation of Well-Formed Parenthesis Strings in Constant Worst-Case Time Ž. JOURNAL OF ALGORITHMS 29, 165173 1998 ARTICLE NO. AL980960 Generaton of Well-Formed Parenthess Strngs n Constant Worst-Case Tme Tmothy R. Walsh Department of Computer Scence, Unersty of Quebec at Montreal,

More information

Price and Quantity Competition Revisited. Abstract

Price and Quantity Competition Revisited. Abstract rce and uantty Competton Revsted X. Henry Wang Unversty of Mssour - Columba Abstract By enlargng the parameter space orgnally consdered by Sngh and Vves (984 to allow for a wder range of cost asymmetry,

More information

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999 FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS by Rchard M. Levch New York Unversty Stern School of Busness Revsed, February 1999 1 SETTING UP THE PROBLEM The bond s beng sold to Swss nvestors for a prce

More information

Scribe: Chris Berlind Date: Feb 1, 2010

Scribe: Chris Berlind Date: Feb 1, 2010 CS/CNS/EE 253: Advanced Topcs n Machne Learnng Topc: Dealng wth Partal Feedback #2 Lecturer: Danel Golovn Scrbe: Chrs Berlnd Date: Feb 1, 2010 8.1 Revew In the prevous lecture we began lookng at algorthms

More information

A DUAL EXTERIOR POINT SIMPLEX TYPE ALGORITHM FOR THE MINIMUM COST NETWORK FLOW PROBLEM

A DUAL EXTERIOR POINT SIMPLEX TYPE ALGORITHM FOR THE MINIMUM COST NETWORK FLOW PROBLEM Yugoslav Journal of Operatons Research Vol 19 (2009), Number 1, 157-170 DOI:10.2298/YUJOR0901157G A DUAL EXTERIOR POINT SIMPLEX TYPE ALGORITHM FOR THE MINIMUM COST NETWORK FLOW PROBLEM George GERANIS Konstantnos

More information

EDC Introduction

EDC Introduction .0 Introducton EDC3 In the last set of notes (EDC), we saw how to use penalty factors n solvng the EDC problem wth losses. In ths set of notes, we want to address two closely related ssues. What are, exactly,

More information

3: Central Limit Theorem, Systematic Errors

3: Central Limit Theorem, Systematic Errors 3: Central Lmt Theorem, Systematc Errors 1 Errors 1.1 Central Lmt Theorem Ths theorem s of prme mportance when measurng physcal quanttes because usually the mperfectons n the measurements are due to several

More information

Games and Decisions. Part I: Basic Theorems. Contents. 1 Introduction. Jane Yuxin Wang. 1 Introduction 1. 2 Two-player Games 2

Games and Decisions. Part I: Basic Theorems. Contents. 1 Introduction. Jane Yuxin Wang. 1 Introduction 1. 2 Two-player Games 2 Games and Decsons Part I: Basc Theorems Jane Yuxn Wang Contents 1 Introducton 1 2 Two-player Games 2 2.1 Zero-sum Games................................ 3 2.1.1 Pure Strateges.............................

More information

Introduction to game theory

Introduction to game theory Introducton to game theory Lectures n game theory ECON5210, Sprng 2009, Part 1 17.12.2008 G.B. Ashem, ECON5210-1 1 Overvew over lectures 1. Introducton to game theory 2. Modelng nteractve knowledge; equlbrum

More information

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of Module 8: Probablty and Statstcal Methods n Water Resources Engneerng Bob Ptt Unversty of Alabama Tuscaloosa, AL Flow data are avalable from numerous USGS operated flow recordng statons. Data s usually

More information

AC : THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS

AC : THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS AC 2008-1635: THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS Kun-jung Hsu, Leader Unversty Amercan Socety for Engneerng Educaton, 2008 Page 13.1217.1 Ttle of the Paper: The Dagrammatc

More information

/ Computational Genomics. Normalization

/ Computational Genomics. Normalization 0-80 /02-70 Computatonal Genomcs Normalzaton Gene Expresson Analyss Model Computatonal nformaton fuson Bologcal regulatory networks Pattern Recognton Data Analyss clusterng, classfcaton normalzaton, mss.

More information

UNIVERSITY OF NOTTINGHAM

UNIVERSITY OF NOTTINGHAM UNIVERSITY OF NOTTINGHAM SCHOOL OF ECONOMICS DISCUSSION PAPER 99/28 Welfare Analyss n a Cournot Game wth a Publc Good by Indraneel Dasgupta School of Economcs, Unversty of Nottngham, Nottngham NG7 2RD,

More information

Solution of periodic review inventory model with general constrains

Solution of periodic review inventory model with general constrains Soluton of perodc revew nventory model wth general constrans Soluton of perodc revew nventory model wth general constrans Prof Dr J Benkő SZIU Gödöllő Summary Reasons for presence of nventory (stock of

More information

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS QUESTIONS 9.1. (a) In a log-log model the dependent and all explanatory varables are n the logarthmc form. (b) In the log-ln model the dependent varable

More information

Cyclic Scheduling in a Job shop with Multiple Assembly Firms

Cyclic Scheduling in a Job shop with Multiple Assembly Firms Proceedngs of the 0 Internatonal Conference on Industral Engneerng and Operatons Management Kuala Lumpur, Malaysa, January 4, 0 Cyclc Schedulng n a Job shop wth Multple Assembly Frms Tetsuya Kana and Koch

More information

A Constant-Factor Approximation Algorithm for Network Revenue Management

A Constant-Factor Approximation Algorithm for Network Revenue Management A Constant-Factor Approxmaton Algorthm for Networ Revenue Management Yuhang Ma 1, Paat Rusmevchentong 2, Ma Sumda 1, Huseyn Topaloglu 1 1 School of Operatons Research and Informaton Engneerng, Cornell

More information

Optimising a general repair kit problem with a service constraint

Optimising a general repair kit problem with a service constraint Optmsng a general repar kt problem wth a servce constrant Marco Bjvank 1, Ger Koole Department of Mathematcs, VU Unversty Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands Irs F.A. Vs Department

More information

arxiv: v2 [math.co] 6 Apr 2016

arxiv: v2 [math.co] 6 Apr 2016 On the number of equvalence classes of nvertble Boolean functons under acton of permutaton of varables on doman and range arxv:1603.04386v2 [math.co] 6 Apr 2016 Marko Carć and Modrag Žvkovć Abstract. Let

More information

A Graph-Theoretic Characterization of AND-OR Deadlocks

A Graph-Theoretic Characterization of AND-OR Deadlocks A Graph-Theoretc Characterzaton of AND-OR Deadlocks Valmr C Barbosa Maro R F Benevdes Programa de Engenhara de Sstemas e Computação, COPPE Insttuto de Matemátca Unversdade Federal do Ro de Janero Caxa

More information

4. Greek Letters, Value-at-Risk

4. Greek Letters, Value-at-Risk 4 Greek Letters, Value-at-Rsk 4 Value-at-Rsk (Hull s, Chapter 8) Math443 W08, HM Zhu Outlne (Hull, Chap 8) What s Value at Rsk (VaR)? Hstorcal smulatons Monte Carlo smulatons Model based approach Varance-covarance

More information

Introduction to PGMs: Discrete Variables. Sargur Srihari

Introduction to PGMs: Discrete Variables. Sargur Srihari Introducton to : Dscrete Varables Sargur srhar@cedar.buffalo.edu Topcs. What are graphcal models (or ) 2. Use of Engneerng and AI 3. Drectonalty n graphs 4. Bayesan Networks 5. Generatve Models and Samplng

More information

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique.

Mode is the value which occurs most frequency. The mode may not exist, and even if it does, it may not be unique. 1.7.4 Mode Mode s the value whch occurs most frequency. The mode may not exst, and even f t does, t may not be unque. For ungrouped data, we smply count the largest frequency of the gven value. If all

More information

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction.

332 Mathematical Induction Solutions for Chapter 14. for every positive integer n. Proof. We will prove this with mathematical induction. 33 Mathematcal Inducton. Solutons for Chapter. Prove that 3 n n n for every postve nteger n. Proof. We wll prove ths wth mathematcal nducton. Observe that f n, ths statement s, whch s obvously true. Consder

More information

Lecture Note 2 Time Value of Money

Lecture Note 2 Time Value of Money Seg250 Management Prncples for Engneerng Managers Lecture ote 2 Tme Value of Money Department of Systems Engneerng and Engneerng Management The Chnese Unversty of Hong Kong Interest: The Cost of Money

More information

Homework 9: due Monday, 27 October, 2008

Homework 9: due Monday, 27 October, 2008 PROBLEM ONE Homework 9: due Monday, 7 October, 008. (Exercses from the book, 6 th edton, 6.6, -3.) Determne the number of dstnct orderngs of the letters gven: (a) GUIDE (b) SCHOOL (c) SALESPERSONS. (Exercses

More information

Fast Laplacian Solvers by Sparsification

Fast Laplacian Solvers by Sparsification Spectral Graph Theory Lecture 19 Fast Laplacan Solvers by Sparsfcaton Danel A. Spelman November 9, 2015 Dsclamer These notes are not necessarly an accurate representaton of what happened n class. The notes

More information

A Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks

A Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks A Dstrbuted Algorthm for Constraned Mult-Robot Tas Assgnment for Grouped Tass Lngzh Luo Robotcs Insttute Carnege Mellon Unversty Pttsburgh, PA 15213 lngzhl@cs.cmu.edu Nlanjan Charaborty Robotcs Insttute

More information

Survey of Math: Chapter 22: Consumer Finance Borrowing Page 1

Survey of Math: Chapter 22: Consumer Finance Borrowing Page 1 Survey of Math: Chapter 22: Consumer Fnance Borrowng Page 1 APR and EAR Borrowng s savng looked at from a dfferent perspectve. The dea of smple nterest and compound nterest stll apply. A new term s the

More information

Lecture Note 1: Foundations 1

Lecture Note 1: Foundations 1 Economcs 703 Advanced Mcroeconomcs Prof. Peter Cramton ecture Note : Foundatons Outlne A. Introducton and Examples B. Formal Treatment. Exstence of Nash Equlbrum. Exstence wthout uas-concavty 3. Perfect

More information

>1 indicates country i has a comparative advantage in production of j; the greater the index, the stronger the advantage. RCA 1 ij

>1 indicates country i has a comparative advantage in production of j; the greater the index, the stronger the advantage. RCA 1 ij 69 APPENDIX 1 RCA Indces In the followng we present some maor RCA ndces reported n the lterature. For addtonal varants and other RCA ndces, Memedovc (1994) and Vollrath (1991) provde more thorough revews.

More information

OCR Statistics 1 Working with data. Section 2: Measures of location

OCR Statistics 1 Working with data. Section 2: Measures of location OCR Statstcs 1 Workng wth data Secton 2: Measures of locaton Notes and Examples These notes have sub-sectons on: The medan Estmatng the medan from grouped data The mean Estmatng the mean from grouped data

More information

Global Optimization in Multi-Agent Models

Global Optimization in Multi-Agent Models Global Optmzaton n Mult-Agent Models John R. Brge R.R. McCormck School of Engneerng and Appled Scence Northwestern Unversty Jont work wth Chonawee Supatgat, Enron, and Rachel Zhang, Cornell 11/19/2004

More information

Cofactorisation strategies for the number field sieve and an estimate for the sieving step for factoring 1024-bit integers

Cofactorisation strategies for the number field sieve and an estimate for the sieving step for factoring 1024-bit integers Cofactorsaton strateges for the number feld seve and an estmate for the sevng step for factorng 1024-bt ntegers Thorsten Klenjung Unversty of Bonn, Department of Mathematcs, Berngstraße 1, D-53115 Bonn,

More information

PREFERENCE DOMAINS AND THE MONOTONICITY OF CONDORCET EXTENSIONS

PREFERENCE DOMAINS AND THE MONOTONICITY OF CONDORCET EXTENSIONS PREFERECE DOMAIS AD THE MOOTOICITY OF CODORCET EXTESIOS PAUL J. HEALY AD MICHAEL PERESS ABSTRACT. An alternatve s a Condorcet wnner f t beats all other alternatves n a parwse majorty vote. A socal choce

More information

CHAPTER 3: BAYESIAN DECISION THEORY

CHAPTER 3: BAYESIAN DECISION THEORY CHATER 3: BAYESIAN DECISION THEORY Decson makng under uncertanty 3 rogrammng computers to make nference from data requres nterdscplnary knowledge from statstcs and computer scence Knowledge of statstcs

More information

Jeffrey Ely. October 7, This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.

Jeffrey Ely. October 7, This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. October 7, 2012 Ths work s lcensed under the Creatve Commons Attrbuton-NonCommercal-ShareAlke 3.0 Lcense. Recap We saw last tme that any standard of socal welfare s problematc n a precse sense. If we want

More information

Minimizing the number of critical stages for the on-line steiner tree problem

Minimizing the number of critical stages for the on-line steiner tree problem Mnmzng the number of crtcal stages for the on-lne stener tree problem Ncolas Thbault, Chrstan Laforest IBISC, Unversté d Evry, Tour Evry 2, 523 place des terrasses, 91000 EVRY France Keywords: on-lne algorthm,

More information

Parsing beyond context-free grammar: Tree Adjoining Grammar Parsing I

Parsing beyond context-free grammar: Tree Adjoining Grammar Parsing I Parsng beyond context-free grammar: Tree donng Grammar Parsng I Laura Kallmeyer, Wolfgang Maer ommersemester 2009 duncton and substtuton (1) Tree donng Grammars (TG) Josh et al. (1975), Josh & chabes (1997):

More information

Tree-based and GA tools for optimal sampling design

Tree-based and GA tools for optimal sampling design Tree-based and GA tools for optmal samplng desgn The R User Conference 2008 August 2-4, Technsche Unverstät Dortmund, Germany Marco Balln, Gulo Barcarol Isttuto Nazonale d Statstca (ISTAT) Defnton of the

More information

2) In the medium-run/long-run, a decrease in the budget deficit will produce:

2) In the medium-run/long-run, a decrease in the budget deficit will produce: 4.02 Quz 2 Solutons Fall 2004 Multple-Choce Questons ) Consder the wage-settng and prce-settng equatons we studed n class. Suppose the markup, µ, equals 0.25, and F(u,z) = -u. What s the natural rate of

More information

- contrast so-called first-best outcome of Lindahl equilibrium with case of private provision through voluntary contributions of households

- contrast so-called first-best outcome of Lindahl equilibrium with case of private provision through voluntary contributions of households Prvate Provson - contrast so-called frst-best outcome of Lndahl equlbrum wth case of prvate provson through voluntary contrbutons of households - need to make an assumpton about how each household expects

More information

Problems to be discussed at the 5 th seminar Suggested solutions

Problems to be discussed at the 5 th seminar Suggested solutions ECON4260 Behavoral Economcs Problems to be dscussed at the 5 th semnar Suggested solutons Problem 1 a) Consder an ultmatum game n whch the proposer gets, ntally, 100 NOK. Assume that both the proposer

More information

International ejournals

International ejournals Avalable onlne at www.nternatonalejournals.com ISSN 0976 1411 Internatonal ejournals Internatonal ejournal of Mathematcs and Engneerng 7 (010) 86-95 MODELING AND PREDICTING URBAN MALE POPULATION OF BANGLADESH:

More information

references Chapters on game theory in Mas-Colell, Whinston and Green

references Chapters on game theory in Mas-Colell, Whinston and Green Syllabus. Prelmnares. Role of game theory n economcs. Normal and extensve form of a game. Game-tree. Informaton partton. Perfect recall. Perfect and mperfect nformaton. Strategy.. Statc games of complete

More information

The Integration of the Israel Labour Force Survey with the National Insurance File

The Integration of the Israel Labour Force Survey with the National Insurance File The Integraton of the Israel Labour Force Survey wth the Natonal Insurance Fle Natale SHLOMO Central Bureau of Statstcs Kanfey Nesharm St. 66, corner of Bach Street, Jerusalem Natales@cbs.gov.l Abstact:

More information

On the Moments of the Traces of Unitary and Orthogonal Random Matrices

On the Moments of the Traces of Unitary and Orthogonal Random Matrices Proceedngs of Insttute of Mathematcs of NAS of Ukrane 2004 Vol. 50 Part 3 1207 1213 On the Moments of the Traces of Untary and Orthogonal Random Matrces Vladmr VASILCHU B. Verkn Insttute for Low Temperature

More information

Finite Math - Fall Section Future Value of an Annuity; Sinking Funds

Finite Math - Fall Section Future Value of an Annuity; Sinking Funds Fnte Math - Fall 2016 Lecture Notes - 9/19/2016 Secton 3.3 - Future Value of an Annuty; Snkng Funds Snkng Funds. We can turn the annutes pcture around and ask how much we would need to depost nto an account

More information

On the Complexity of Fair Coin Flipping

On the Complexity of Fair Coin Flipping On the Complexty of Far Con Flppng ftach Hatner Nkolaos Makryanns Eran Omr Aprl 16, 2018 Abstract n ther breakthrough result, Moran et al. Journal of Cryptology 16 show how to construct an r-round two-party

More information

Chapter 5 Student Lecture Notes 5-1

Chapter 5 Student Lecture Notes 5-1 Chapter 5 Student Lecture Notes 5-1 Basc Busness Statstcs (9 th Edton) Chapter 5 Some Important Dscrete Probablty Dstrbutons 004 Prentce-Hall, Inc. Chap 5-1 Chapter Topcs The Probablty Dstrbuton of a Dscrete

More information

Comparison of Singular Spectrum Analysis and ARIMA

Comparison of Singular Spectrum Analysis and ARIMA Int. Statstcal Inst.: Proc. 58th World Statstcal Congress, 0, Dubln (Sesson CPS009) p.99 Comparson of Sngular Spectrum Analss and ARIMA Models Zokae, Mohammad Shahd Behesht Unverst, Department of Statstcs

More information

A Single-Product Inventory Model for Multiple Demand Classes 1

A Single-Product Inventory Model for Multiple Demand Classes 1 A Sngle-Product Inventory Model for Multple Demand Classes Hasan Arslan, 2 Stephen C. Graves, 3 and Thomas Roemer 4 March 5, 2005 Abstract We consder a sngle-product nventory system that serves multple

More information

Taxation and Externalities. - Much recent discussion of policy towards externalities, e.g., global warming debate/kyoto

Taxation and Externalities. - Much recent discussion of policy towards externalities, e.g., global warming debate/kyoto Taxaton and Externaltes - Much recent dscusson of polcy towards externaltes, e.g., global warmng debate/kyoto - Increasng share of tax revenue from envronmental taxaton 6 percent n OECD - Envronmental

More information

Evaluating Performance

Evaluating Performance 5 Chapter Evaluatng Performance In Ths Chapter Dollar-Weghted Rate of Return Tme-Weghted Rate of Return Income Rate of Return Prncpal Rate of Return Daly Returns MPT Statstcs 5- Measurng Rates of Return

More information

Note on Cubic Spline Valuation Methodology

Note on Cubic Spline Valuation Methodology Note on Cubc Splne Valuaton Methodology Regd. Offce: The Internatonal, 2 nd Floor THE CUBIC SPLINE METHODOLOGY A model for yeld curve takes traded yelds for avalable tenors as nput and generates the curve

More information

Supplementary material for Non-conjugate Variational Message Passing for Multinomial and Binary Regression

Supplementary material for Non-conjugate Variational Message Passing for Multinomial and Binary Regression Supplementary materal for Non-conjugate Varatonal Message Passng for Multnomal and Bnary Regresson October 9, 011 1 Alternatve dervaton We wll focus on a partcular factor f a and varable x, wth the am

More information

Multifactor Term Structure Models

Multifactor Term Structure Models 1 Multfactor Term Structure Models A. Lmtatons of One-Factor Models 1. Returns on bonds of all maturtes are perfectly correlated. 2. Term structure (and prces of every other dervatves) are unquely determned

More information

Partial ARTIAL Incompatible based Lower Bound of NC* For MAX-CSPs

Partial ARTIAL Incompatible based Lower Bound of NC* For MAX-CSPs Egyptan Computer Scence Journal,ECS,Vol. 37 No., January 03 ISSN-0-586 Partal ARTIAL Incompatble based Lower Bound of NC* For MAX-CSPs Ashraf M. Bhery, Soher M. Khams, and Wafaa A. Kabela Dvson of Computer

More information

Trivial lump sum R5.1

Trivial lump sum R5.1 Trval lump sum R5.1 Optons form Once you have flled n ths form, please return t wth the documents we have requested. You can ether post or emal the form and the documents to us. Premer PO Box 108 BLYTH

More information

Maximum Likelihood Estimation of Isotonic Normal Means with Unknown Variances*

Maximum Likelihood Estimation of Isotonic Normal Means with Unknown Variances* Journal of Multvarate Analyss 64, 183195 (1998) Artcle No. MV971717 Maxmum Lelhood Estmaton of Isotonc Normal Means wth Unnown Varances* Nng-Zhong Sh and Hua Jang Northeast Normal Unversty, Changchun,Chna

More information

Fall 2017 Social Sciences 7418 University of Wisconsin-Madison Problem Set 3 Answers

Fall 2017 Social Sciences 7418 University of Wisconsin-Madison Problem Set 3 Answers ublc Affars 854 enze D. Chnn Fall 07 Socal Scences 748 Unversty of Wsconsn-adson roblem Set 3 Answers Due n Lecture on Wednesday, November st. " Box n" your answers to the algebrac questons.. Fscal polcy

More information

A Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect

A Comparison of Statistical Methods in Interrupted Time Series Analysis to Estimate an Intervention Effect Transport and Road Safety (TARS) Research Joanna Wang A Comparson of Statstcal Methods n Interrupted Tme Seres Analyss to Estmate an Interventon Effect Research Fellow at Transport & Road Safety (TARS)

More information

A New Uniform-based Resource Constrained Total Project Float Measure (U-RCTPF) Roni Levi. Research & Engineering, Haifa, Israel

A New Uniform-based Resource Constrained Total Project Float Measure (U-RCTPF) Roni Levi. Research & Engineering, Haifa, Israel Management Studes, August 2014, Vol. 2, No. 8, 533-540 do: 10.17265/2328-2185/2014.08.005 D DAVID PUBLISHING A New Unform-based Resource Constraned Total Project Float Measure (U-RCTPF) Ron Lev Research

More information

arxiv:cond-mat/ v1 [cond-mat.other] 28 Nov 2004

arxiv:cond-mat/ v1 [cond-mat.other] 28 Nov 2004 arxv:cond-mat/0411699v1 [cond-mat.other] 28 Nov 2004 Estmatng Probabltes of Default for Low Default Portfolos Katja Pluto and Drk Tasche November 23, 2004 Abstract For credt rsk management purposes n general,

More information