Algorithmic Incentives: New Twist on 30 Year- Old Work Could Lead to Better Ways of Structuring Contracts 25 April 2012, by Larry Hardesty
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On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs*
On the Randomness Complexity of Interactive Proofs and Statistical Zero-Knowledge Proofs* Benny Applebaum† Eyal Golombek* Abstract We study the randomness complexity of interactive proofs and zero-knowledge proofs. In particular, we ask whether it is possible to reduce the randomness complexity, R, of the verifier to be comparable with the number of bits, CV , that the verifier sends during the interaction. We show that such randomness sparsification is possible in several settings. Specifically, unconditional sparsification can be obtained in the non-uniform setting (where the verifier is modelled as a circuit), and in the uniform setting where the parties have access to a (reusable) common-random-string (CRS). We further show that constant-round uniform protocols can be sparsified without a CRS under a plausible worst-case complexity-theoretic assumption that was used previously in the context of derandomization. All the above sparsification results preserve statistical-zero knowledge provided that this property holds against a cheating verifier. We further show that randomness sparsification can be applied to honest-verifier statistical zero-knowledge (HVSZK) proofs at the expense of increasing the communica- tion from the prover by R−F bits, or, in the case of honest-verifier perfect zero-knowledge (HVPZK) by slowing down the simulation by a factor of 2R−F . Here F is a new measure of accessible bit complexity of an HVZK proof system that ranges from 0 to R, where a maximal grade of R is achieved when zero- knowledge holds against a “semi-malicious” verifier that maliciously selects its random tape and then plays honestly. -
Oacerts: Oblivious Attribute Certificates
OACerts: Oblivious Attribute Certificates Jiangtao Li and Ninghui Li CERIAS and Department of Computer Science, Purdue University 250 N. University Street, West Lafayette, IN 47907 {jtli,ninghui}@cs.purdue.edu Abstract. We propose Oblivious Attribute Certificates (OACerts), an attribute certificate scheme in which a certificate holder can select which attributes to use and how to use them. In particular, a user can use attribute values stored in an OACert obliviously, i.e., the user obtains a service if and only if the attribute values satisfy the policy of the service provider, yet the service provider learns nothing about these attribute values. This way, the service provider’s access con- trol policy is enforced in an oblivious fashion. To enable the oblivious access control using OACerts, we propose a new cryp- tographic primitive called Oblivious Commitment-Based Envelope (OCBE). In an OCBE scheme, Bob has an attribute value committed to Alice and Alice runs a protocol with Bob to send an envelope (encrypted message) to Bob such that: (1) Bob can open the envelope if and only if his committed attribute value sat- isfies a predicate chosen by Alice, (2) Alice learns nothing about Bob’s attribute value. We develop provably secure and efficient OCBE protocols for the Pedersen commitment scheme and predicates such as =, ≥, ≤, >, <, 6= as well as logical combinations of them. 1 Introduction In Trust Management and certificate-based access control Systems [3, 40, 19, 11, 34, 33], access control decisions are based on attributes of requesters, which are estab- lished by digitally signed certificates. Each certificate associates a public key with the key holder’s identity and/or attributes such as employer, group membership, credit card information, birth-date, citizenship, and so on. -
Simple Doubly-Efficient Interactive Proof Systems for Locally
Electronic Colloquium on Computational Complexity, Revision 3 of Report No. 18 (2017) Simple doubly-efficient interactive proof systems for locally-characterizable sets Oded Goldreich∗ Guy N. Rothblumy September 8, 2017 Abstract A proof system is called doubly-efficient if the prescribed prover strategy can be implemented in polynomial-time and the verifier’s strategy can be implemented in almost-linear-time. We present direct constructions of doubly-efficient interactive proof systems for problems in P that are believed to have relatively high complexity. Specifically, such constructions are presented for t-CLIQUE and t-SUM. In addition, we present a generic construction of such proof systems for a natural class that contains both problems and is in NC (and also in SC). The proof systems presented by us are significantly simpler than the proof systems presented by Goldwasser, Kalai and Rothblum (JACM, 2015), let alone those presented by Reingold, Roth- blum, and Rothblum (STOC, 2016), and can be implemented using a smaller number of rounds. Contents 1 Introduction 1 1.1 The current work . 1 1.2 Relation to prior work . 3 1.3 Organization and conventions . 4 2 Preliminaries: The sum-check protocol 5 3 The case of t-CLIQUE 5 4 The general result 7 4.1 A natural class: locally-characterizable sets . 7 4.2 Proof of Theorem 1 . 8 4.3 Generalization: round versus computation trade-off . 9 4.4 Extension to a wider class . 10 5 The case of t-SUM 13 References 15 Appendix: An MA proof system for locally-chracterizable sets 18 ∗Department of Computer Science, Weizmann Institute of Science, Rehovot, Israel. -
A Zero Knowledge Sumcheck and Its Applications
A Zero Knowledge Sumcheck and its Applications Alessandro Chiesa Michael A. Forbes Nicholas Spooner [email protected] [email protected] [email protected] UC Berkeley Simons Institute for the Theory of Computing University of Toronto and UC Berkeley April 6, 2017 Abstract Many seminal results in Interactive Proofs (IPs) use algebraic techniques based on low-degree polynomials, the study of which is pervasive in theoretical computer science. Unfortunately, known methods for endowing such proofs with zero knowledge guarantees do not retain this rich algebraic structure. In this work, we develop algebraic techniques for obtaining zero knowledge variants of proof protocols in a way that leverages and preserves their algebraic structure. Our constructions achieve unconditional (perfect) zero knowledge in the Interactive Probabilistically Checkable Proof (IPCP) model of Kalai and Raz [KR08] (the prover first sends a PCP oracle, then the prover and verifier engage in an Interactive Proof in which the verifier may query the PCP). Our main result is a zero knowledge variant of the sumcheck protocol [LFKN92] in the IPCP model. The sumcheck protocol is a key building block in many IPs, including the protocol for polynomial-space computation due to Shamir [Sha92], and the protocol for parallel computation due to Goldwasser, Kalai, and Rothblum [GKR15]. A core component of our result is an algebraic commitment scheme, whose hiding property is guaranteed by algebraic query complexity lower bounds [AW09; JKRS09]. This commitment scheme can then be used to considerably strengthen our previous work [BCFGRS16] that gives a sumcheck protocol with much weaker zero knowledge guarantees, itself using algebraic techniques based on algorithms for polynomial identity testing [RS05; BW04]. -
Zero Knowledge and Soundness Are Symmetric∗
Zero Knowledge and Soundness are Symmetric∗ Shien Jin Ong Salil Vadhan Division of Engineering and Applied Sciences Harvard University Cambridge, Massachusetts, USA. E-mail: {shienjin,salil}@eecs.harvard.edu November 14, 2006 Abstract We give a complexity-theoretic characterization of the class of problems in NP having zero- knowledge argument systems that is symmetric in its treatment of the zero knowledge and the soundness conditions. From this, we deduce that the class of problems in NP ∩ coNP having zero-knowledge arguments is closed under complement. Furthermore, we show that a problem in NP has a statistical zero-knowledge argument system if and only if its complement has a computational zero-knowledge proof system. What is novel about these results is that they are unconditional, i.e. do not rely on unproven complexity assumptions such as the existence of one-way functions. Our characterization of zero-knowledge arguments also enables us to prove a variety of other unconditional results about the class of problems in NP having zero-knowledge arguments, such as equivalences between honest-verifier and malicious-verifier zero knowledge, private coins and public coins, inefficient provers and efficient provers, and non-black-box simulation and black-box simulation. Previously, such results were only known unconditionally for zero-knowledge proof systems, or under the assumption that one-way functions exist for zero-knowledge argument systems. Keywords: zero-knowledge argument systems, statistical zero knowledge, complexity classes, clo- sure under complement, distributional one-way functions. ∗Both the authors were supported by NSF grant CNS-0430336 and ONR grant N00014-04-1-0478. -
Pseudorandomness and Combinatorial Constructions
Pseudorandomness and Combinatorial Constructions Luca Trevisan∗ September 20, 2018 Abstract In combinatorics, the probabilistic method is a very powerful tool to prove the existence of combinatorial objects with interesting and useful properties. Explicit constructions of objects with such properties are often very difficult, or unknown. In computer science, probabilistic al- gorithms are sometimes simpler and more efficient than the best known deterministic algorithms for the same problem. Despite this evidence for the power of random choices, the computational theory of pseu- dorandomness shows that, under certain complexity-theoretic assumptions, every probabilistic algorithm has an efficient deterministic simulation and a large class of applications of the the probabilistic method can be converted into explicit constructions. In this survey paper we describe connections between the conditional “derandomization” re- sults of the computational theory of pseudorandomness and unconditional explicit constructions of certain combinatorial objects such as error-correcting codes and “randomness extractors.” 1 Introduction 1.1 The Probabilistic Method in Combinatorics In extremal combinatorics, the probabilistic method is the following approach to proving existence of objects with certain properties: prove that a random object has the property with positive probability. This simple idea has beem amazingly successful, and it gives the best known bounds for arXiv:cs/0601100v1 [cs.CC] 23 Jan 2006 most problems in extremal combinatorics. The idea was introduced (and, later, greatly developed) by Paul Erd˝os [18], who originally applied it to the following question: define R(k, k) to be the minimum value n such that every graph on n vertices has either an independent set of size at least k or a clique of size at least k;1 It was known that R(k, k) is finite and that it is at most 4k, and the question was to prove a lower bound. -
Atom: Horizontally Scaling Strong Anonymity
Atom: Horizontally Scaling Strong Anonymity Albert Kwon Henry Corrigan-Gibbs Srinivas Devadas Bryan Ford MIT Stanford MIT EPFL Abstract Atom is an anonymous messaging system that pro- This scalability property has enabled Tor to grow to handle tects against traffic-analysis attacks. Unlike many prior sys- hundreds of thousands to millions of users [3]. However, the tems, each Atom server touches only a small fraction of the fact that Tor provides low-latency anonymity also makes the total messages routed through the network. As a result, the system vulnerable to a variety of deanonymization attacks [9, system’s capacity scales near-linearly with the number of 17, 31, 44, 56, 74, 75]. servers. At the same time, each Atom user benefits from In this paper, we present Atom, an anonymous messag- “best possible” anonymity: a user is anonymous among all ing system that takes important steps towards marrying the honest users of the system, even against an active adversary best aspects of these two architectural strategies. Like Tor, who monitors the entire network, a portion of the system’s Atom scales horizontally: adding more servers to the network servers, and any number of malicious users. The architectural increases the system’s overall capacity. Like mix-net- and DC- ideas behind Atom have been known in theory, but putting net-based systems, Atom provides clear security properties them into practice requires new techniques for (1) avoiding under precise assumptions. heavy general-purpose multi-party computation protocols, Atom implements an anonymous broadcast primitive for (2) defeating active attacks by malicious servers at minimal short, latency-tolerant messages. -
DRAFT -- DRAFT -- Appendix
Appendix A Appendix A.1 Probability theory A random variable is a mapping from a probability space to R.Togivean example, the probability space could be that of all 2n possible outcomes of n tosses of a fair coin, and Xi is the random variable that is 1 if the ith toss is a head, and is 0 otherwise. An event is a subset of the probability space. The following simple bound —called the union bound—is often used in the book. For every set of events B1,B2,...,Bk, k ∪k ≤ Pr[ i=1Bi] Pr[Bi]. (A.1) i=1 A.1.1 The averaging argument We list various versions of the “averaging argument.” Sometimes we give two versions of the same result, one as a fact about numbers and one as a fact about probability spaces. Lemma A.1 If a1,a2,...,an are some numbers whose average is c then some ai ≥ c. Lemma A.2 (“The Probabilistic Method”) If X is a random variable which takes values from a finite set and E[X]=μ then the event “X ≥ μ” has nonzero probability. Corollary A.3 If Y is a real-valued function of two random variables x, y then there is a choice c for y such that E[Y (x, c)] ≥ E[Y (x, y)]. 403 DRAFT -- DRAFT -- DRAFT -- DRAFT -- DRAFT -- 404 APPENDIX A. APPENDIX Lemma A.4 If a1,a2,...,an ≥ 0 are numbers whose average is c then the fraction of ai’s that are greater than (resp., at least) kc is less than (resp, at most) 1/k. -
Learning to Impersonate
Learning to Impersonate Moni Naor¤ Guy N. Rothblumy Abstract Consider Alice, who is interacting with Bob. Alice and Bob have some shared secret which helps Alice identify Bob-impersonators. Now consider Eve, who knows Alice and Bob, but does not know their shared secret. Eve would like to impersonate Bob and \fool" Alice without knowing the secret. If Eve is computationally unbounded, how long does she need to observe Alice and Bob interacting before she can successfully impersonate Bob? What is a good strategy for Eve in this setting? If Eve runs in polynomial time, and if there exists a one-way function, then it is not hard to see that Alice and Bob may be \safe" from impersonators, but is the existence of one-way functions an essential condition? Namely, if one-way functions do not exist, can an e±cient Eve always impersonate Bob? In this work we consider these natural questions from the point of view of Ever, who is trying to observe Bob and learn to impersonate him. We formalize this setting in a new compu- tational learning model of learning adaptively changing distributions (ACDs), which we believe captures a wide variety of natural learning tasks and is of interest from both cryptographic and computational learning points of view. We present a learning algorithm that Eve can use to successfully learn to impersonate Bob in the information-theoretic setting. We also show that in the computational setting an e±cient Eve can learn to impersonate any e±cient Bob if and only if one-way function do not exist. -
Advances and Open Problems in Federated Learning
Advances and Open Problems in Federated Learning Peter Kairouz7* H. Brendan McMahan7∗ Brendan Avent21 Aurelien´ Bellet9 Mehdi Bennis19 Arjun Nitin Bhagoji13 Kallista Bonawitz7 Zachary Charles7 Graham Cormode23 Rachel Cummings6 Rafael G.L. D’Oliveira14 Hubert Eichner7 Salim El Rouayheb14 David Evans22 Josh Gardner24 Zachary Garrett7 Adria` Gascon´ 7 Badih Ghazi7 Phillip B. Gibbons2 Marco Gruteser7;14 Zaid Harchaoui24 Chaoyang He21 Lie He 4 Zhouyuan Huo 20 Ben Hutchinson7 Justin Hsu25 Martin Jaggi4 Tara Javidi17 Gauri Joshi2 Mikhail Khodak2 Jakub Konecnˇ y´7 Aleksandra Korolova21 Farinaz Koushanfar17 Sanmi Koyejo7;18 Tancrede` Lepoint7 Yang Liu12 Prateek Mittal13 Mehryar Mohri7 Richard Nock1 Ayfer Ozg¨ ur¨ 15 Rasmus Pagh7;10 Hang Qi7 Daniel Ramage7 Ramesh Raskar11 Mariana Raykova7 Dawn Song16 Weikang Song7 Sebastian U. Stich4 Ziteng Sun3 Ananda Theertha Suresh7 Florian Tramer` 15 Praneeth Vepakomma11 Jianyu Wang2 Li Xiong5 Zheng Xu7 Qiang Yang8 Felix X. Yu7 Han Yu12 Sen Zhao7 1Australian National University, 2Carnegie Mellon University, 3Cornell University, 4Ecole´ Polytechnique Fed´ erale´ de Lausanne, 5Emory University, 6Georgia Institute of Technology, 7Google Research, 8Hong Kong University of Science and Technology, 9INRIA, 10IT University of Copenhagen, 11Massachusetts Institute of Technology, 12Nanyang Technological University, 13Princeton University, 14Rutgers University, 15Stanford University, 16University of California Berkeley, 17 University of California San Diego, 18University of Illinois Urbana-Champaign, 19University of Oulu, 20University of Pittsburgh, 21University of Southern California, 22University of Virginia, 23University of Warwick, 24University of Washington, 25University of Wisconsin–Madison arXiv:1912.04977v3 [cs.LG] 9 Mar 2021 Abstract Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. -
Some Estimated Likelihoods for Computational Complexity
Some Estimated Likelihoods For Computational Complexity R. Ryan Williams MIT CSAIL & EECS, Cambridge MA 02139, USA Abstract. The editors of this LNCS volume asked me to speculate on open problems: out of the prominent conjectures in computational com- plexity, which of them might be true, and why? I hope the reader is entertained. 1 Introduction Computational complexity is considered to be a notoriously difficult subject. To its practitioners, there is a clear sense of annoying difficulty in complexity. Complexity theorists generally have many intuitions about what is \obviously" true. Everywhere we look, for every new complexity class that turns up, there's another conjectured lower bound separation, another evidently intractable prob- lem, another apparent hardness with which we must learn to cope. We are sur- rounded by spectacular consequences of all these obviously true things, a sharp coherent world-view with a wonderfully broad theory of hardness and cryptogra- phy available to us, but | gosh, it's so annoying! | we don't have a clue about how we might prove any of these obviously true things. But we try anyway. Much of the present cluelessness can be blamed on well-known \barriers" in complexity theory, such as relativization [BGS75], natural properties [RR97], and algebrization [AW09]. Informally, these are collections of theorems which demonstrate strongly how the popular and intuitive ways that many theorems were proved in the past are fundamentally too weak to prove the lower bounds of the future. { Relativization and algebrization show that proof methods in complexity theory which are \invariant" under certain high-level modifications to the computational model (access to arbitrary oracles, or low-degree extensions thereof) are not “fine-grained enough" to distinguish (even) pairs of classes that seem to be obviously different, such as NEXP and BPP. -
Lecture 3: Interactive Proofs and Zero-Knowledge Instructors: Henry Corrigan-Gibbs, Sam Kim, David J
CS 355 { Topics in Cryptography April 9, 2018 Lecture 3: Interactive Proofs and Zero-Knowledge Instructors: Henry Corrigan-Gibbs, Sam Kim, David J. Wu So far in the class, we have only covered basic cryptographic primitives like PRGs, PRFs, Digital Signatures, and so on. These primitives consist of non-interactive \one-shot" algorithms that satisfy some specific security properties. In the real world, these primitives are used as \tools" that are part of bigger and more complicated interactive protocols. In the next few lectures, we are going to discuss how to analyze these type of interactive protocols. What does it mean for an interactive protocol to be secure? How do we prove security? As a first step towards this goal, we are going to discuss interactive proof systems and zero-knowledge in this lecture. This will be our first introduction to a simulation-based notion of security, which is a very important concept in cryptography. 1 Interactive Proofs Informally, the goal of a proof is to convince someone that a certain statement is true. A statement can consist of expressions such as \N is the product of two 1024-bit primes" or \(g; h) is such that h = ga and a is odd". It is not hard to see how the ability to prove such statements can be useful for cryptographic applications. Languages. To treat proof systems in a rigorous manner, let's first formalize what a statement is. Following the complexity theoretic conventions, we will treat statements with respect to an instance of a language L. Recall that a language is simply a set of strings L ⊆ f0; 1g∗.