Collective Coin-Flipping is a classical problem where n computationally unbounded processors are trying to generate a random bit in a setting where only a single broadcast channel is available for communication. The protocol is said to be b(n)-resilient if any adversary that can corrupt up to b(n) players, still cannot bias the coin to some desired outcome almost certainly. The problem is extensively studied for the case of non-adaptive adversaries who have to decide which players to corrupt before the protocol starts. In particular, it is well-known that the optimum resilience threshold is n=2 in this case. However, none of these protocols is resilient against an adaptive adversary who can corrupt just a single player in the course of the execution. In fact, Ben-Or and Linial [BL90] conjectured that the adaptive adversary is much more powerful than the non-adaptive adversary. More specically, that the optimal resilience threshold for adaptive adversaries is only O( p n) (which is achieved by a simple "majority " protocol). We give strong evidence towards this conjecture by showing that no black-box transformation from any statically secure coin-flipping protocol can yield an adaptively secure protocol tolerating
Collective Coin-Flipping is a classical problem where n computationally unbounded processors are trying to generate a random bit in a setting where only a single broadcast channel is available for communication. The protocol is said to be b(n)-resilient if any adversary that can corrupt up to b(n) players, still cannot bias the coin to some desired outcome almost certainly. The problem is extensively studied for the case of non-adaptive adversaries who have to decide which players to corrupt before the protocol starts. In particular, it is well-known that the optimum resilience threshold is n=2 in this case. However, none of these protocols is resilient against an adaptive adversary who can corrupt just a single player in the course of the execution. In fact, Ben-Or and Linial [BL90] conjectured that the adaptive adversary is much more powerful than the non-adaptive adversary. More specically, that the optimal resilience threshold for adaptive adversaries is only O( p n) (which is achieved by a simple "majority " protocol). We give strong evidence towards this conjecture by showing that no black-box transformation from any statically secure coin-flipping protocol can yield an adaptively secure protocol tolerating
In a distributed coin-flipping protocol, Blum [ACM Transactions on Computer Systems ’83], the parties try to output a common (close to) uniform bit, even when some adversarially chosen parties try to bias the common output. In an adaptively secure full-information coin flip, Ben-Or and Linial [FOCS ’85], the parties communicate over a broadcast channel, and a computationally unbounded adversary can choose which parties to corrupt along the protocol execution. Ben-Or and Linial proved that the n -party majority protocol is resilient to O(n) corruptions, and conjectured this is a tight upper bound for any n -party protocol (of any round complexity). Their conjecture was proved to be correct, up to polylogarithmic factors, for single-turn (each party sends a single message) single-bit (a message is one bit) protocols Lichtenstein et al. [Combinatorica ’89], symmetric protocols Goldwasser et al. [ICALP ’15], and recently for (arbitrary message length) single-turn protocols Tauman Kalai et al. [DISC ’18]. Yet, the question of many-turn protocols was left entirely open. In this work, we close the above gap, proving that no n -party protocol (of any round complexity) is resilient to Ω(n⋅log3n) adaptive corruptions. Namely, majority is the optimal coin-flipping protocol against adaptive adversaries (up to polylogarithmic factors).
The full-information model was introduced by Ben-Or and Linial in 1985 to study collective coin-flipping: the problem of generating a common bounded-bias bit in a network of n players with t=t(n) faults. They showed that the majority protocol can tolerate t=O(n) adaptive corruptions, and conjectured that this is optimal in the adaptive setting. Lichtenstein, Linial, and Saks proved that the conjecture holds for protocols in which each player sends a single bit. Their result has been the main progress on the conjecture in the last 30 years. In this work we revisit this question and ask: what about protocols involving longer messages? Can increased communication allow for a larger fraction of faulty players? We introduce a model of strong adaptive corruptions, where in each round, the adversary sees all messages sent by honest parties and, based on the message content, decides whether to corrupt a party (and intercept his message) or not. We prove that any one-round coin-flipping protocol, regardless of message length, is secure against at most O~(n) strong adaptive corruptions. Thus, increased message length does not help in this setting. We then shed light on the connection between adaptive and strongly adaptive adversaries, by proving that for any symmetric one-round coin-flipping protocol secure against t adaptive corruptions, there is a symmetric one-round coin-flipping protocol secure against t strongly adaptive corruptions. Returning to the standard adaptive model, we can now prove that any symmetric one-round protocol with arbitrarily long messages can tolerate at most O~(n) adaptive corruptions. At the heart of our results lies a novel use of the Minimax Theorem and a new technique for converting any one-round secure protocol into a protocol with messages of polylog(n) bits. This technique may be of independent interest.
Ben-Or and Linial (1985) introduced the full information model for coin-tossing protocols involvingn-processors with unbounded computational power using a common broadcast channel for all their communications. A bias-Xcoin-tossing protocol outputs 1 with probabilityX; otherwise, it outputs 0 with probability (1−X). A coin-tossing protocol's insecurity is the maximum change in the output distribution (in the statistical distance) that an adversary can cause. This work considers an adversary who monitors the protocol's communication and intervenes at most once by restarting the processor who just broadcast her message. For a given toleranceε, our objective is to use the minimum number of processors, ensuring that this adversary can only change the output distribution by at mostϵ. Historically, the “threshold coin-tossing protocols” have been optimal or asymptotically optimal against various adversary models. However, for our model, Khorasgani, Maji, and Mukherjee (2019) prove the existence of coin-tossing protocols that achieve the same tolerance as the threshold protocols using a smaller number of processors. Unfortunately, their protocol is not computationally efficient. Towards this objective, for anyx∈(0,1)andn∈N, this paper presents computationally efficient coin-tossing protocols approximating the new protocols of Khorasgani, Maji, and Mukherjee (2019). This protocol's running time is linear in the inverse of the accuracy parameter of this approximation, which can be set arbitrarily small.
Two main goals of modern cryptography are to identify the minimal assumptions necessary to construct secure cryptographic primitives as well as to construct secure protocols in strong and realistic adversarial models. In this thesis, we address both of these fundamental questions. In the first part of this thesis, we present results on the black-box complexity of two basic cryptographic primitives: non-malleable encryption and optimally-fair coin tossing. Black-box reductions are reductions in which both the underlying primitive as well as the adversary are accessed only in an input-output (or black-box) manner. Most known cryptographic reductions are black-box. Moreover, black-box reductions are typically more efficient than non-black-box reductions. Thus, the black-box complexity of cryptographic primitives is a meaningful and important area of study which allows us to gain insight into the primitive. We study the black box complexity of non-malleable encryption and optimally-fair coin tossing, showing a positive result for the former and a negative one for the latter. Non-malleable encryption is a strong security notion for public-key encryption, guaranteeing that it is impossible to "maul" a ciphertext of a message m into a ciphertext of a related message. This security guarantee is essential for many applications such as auctions. We show how to transform, in a black-box manner, any public-key encryption scheme satisfying a weak form of security, semantic security, to a scheme satisfying non-malleability. Coin tossing is perhaps the most basic cryptographic primitive, allowing two distrustful parties to flip a coin whose outcome is 0 or 1 with probability 1/2. A fair coin tossing protocol is one in which the outputted bit is unbiased, even in the case where one of the parties may abort early. However, in the setting where parties may abort early, there is always a strategy for one of the parties to impose bias of Omega(1/r) in an r-round protocol. Thus, achieving bias of O(1/r) in r rounds is optimal, and it was recently shown that optimally-fair coin tossing can be achieved via a black-box reduction to oblivious transfer. We show that it cannot be achieved via a black-box reduction to one-way function, unless the number of rounds is at least Omega(n/log n), where n is the input/output length of the one-way function. In the second part of this thesis, we present protocols for multiparty computation (MPC) in the Universal Composability (UC) model that are secure against malicious, adaptive adversaries. In the standard model, security is only guaranteed in a stand-alone setting; however, nothing is guaranteed when multiple protocols are arbitrarily composed. In contrast, the UC model, introduced by (Canetti, 2000), considers the execution of an unbounded number of concurrent protocols, in an arbitrary, and adversarially controlled network environment. Another drawback of the standard model is that the adversary must decide which parties to corrupt before the execution of the protocol commences. A more realistic model allows the adversary to adaptively choose which parties to corrupt based on its evolving view during the protocol. In our work we consider the the adaptive UC model, which combines these two security requirements by allowing both arbitrary composition of protocols and adaptive corruption of parties. In our first result, we introduce an improved, efficient construction of non-committing encryption (NCE) with optimal round complexity, from a weaker primitive we introduce called trapdoor-simulatable public key encryption (PKE). NCE is a basic primitive necessary to construct protocols secure under adaptive corruptions and in particular, is used to construct oblivious transfer (OT) protocols secure against semi-honest, adaptive adversaries. Additionally, we show how to realize trapdoor-simulatable PKE from hardness of factoring Blum integers, thus achieving the first construction of NCE from hardness of factoring. In our second result, we present a compiler for transforming an OT protocol secure against a semi-honest, adaptive adversary into one that is secure against a malicious, adaptive adversary. Our compiler achieves security in the UC model, assuming access to an ideal commitment functionality, and improves over previous work achieving the same security guarantee in two ways: it uses black-box access to the underlying protocol and achieves a constant multiplicative overhead in the round complexity. Combining our two results with the work of (Ishai et al., 2008), we obtain the first black-box construction of UC and adaptively secure MPC from trapdoor-simulatable PKE and the ideal commitment functionality.
Ben-Or and Linial (1985) introduced the full information model for coin-tossing protocols involvingnprocessors with unbounded computational power using a common broadcast channel for all their communications. For most adversarial settings, the characterization of the exact or asymptotically optimal protocols remains open. Furthermore, even for the settings where near-optimal asymptotic constructions are known, the exact constants or poly-logarithmic multiplicative factors involved are not entirely well-understood. This work studiesn-processor coin-tossing protocols where every processor broadcasts an arbitrary-length message once. An adaptive Byzantine adversary, based on the messages broadcast so far, can corruptk=1processor. A bias-Xcoin-tossing protocol outputs 1 with probabilityX; otherwise, it outputs 0 with probability (1−X). A coin-tossing protocol's insecurity is the maximum change in the output distribution (in the statistical distance) that a Byzantine adversary can cause. Our objective is to identify bias-Xcoin-tossing protocols achieving near-optimal minimum insecurity for everyX∈[0,1]. Lichtenstein, Linial, and Saks (1989) studied bias-Xcoin-tossing protocols in this adversarial model where each party broadcasts an independent and uniformly random bit. They proved that the elegant “threshold coin-tossing protocols” are optimal for allnandk. Furthermore, Goldwasser, Kalai, and Park (2015), Kalai, Komargodski, and Raz (2018), and Haitner and Karidi-Heller (2020) prove thatk=O(n⋅polylog(n)) corruptions suffice to fix the output of any bias-Xcoin-tossing protocol. These results encompass parties who send arbitrary-length messages, and each processor has multiple turns to reveal its entire message. We use an inductive approach to constructing coin-tossing protocols using a potential function as a proxy for measuring any bias-Xcoin-tossing protocol's susceptibility to attacks in our adversarial model. Our technique is inherently constructive and yields protocols that minimize the potential function. It is incidentally the case that the threshold protocols minimize the potential function, even for arbitrary-length messages. We demonstrate that these coin-tossing protocols' insecurity is a 2-approximation of the optimal protocol in our adversarial model. For any otherX∈[0,1]that threshold protocols cannot realize, we prove that an appropriate (convex) combination of the threshold protocols is a 4-approximation of the optimal protocol. Finally, these results entail new (vertex) isoperimetric inequalities for density-Xsubsets of product spaces of arbitrary-size alphabets.
In a multi-party fair coin-flipping protocol, the parties output a common (close to) unbiased bit, even when some corrupted parties try to bias the output. In this work we focus on the case of dishonest majority, ie at least half of the parties can be corrupted. [19] [STOC 1986] has shown that in any m-round coin-flipping protocol the corrupted parties can bias the honest parties’ common output bit by Θ(1/m). For more than two decades the best known coin-flipping protocols against majority was the protocol of [9] [Manuscript 1985], who presented a t-party, m-round protocol with bias This was changed by the breakthrough result of [42] [TCC 2009], who constructed an m-round, two-party coin-flipping protocol with optimal bias Θ(1/m). Recently, [32] [STOC 14] constructed an m-round, three-party coin-flipping protocol with bias O(log3 m/m). Still for the case of more than three parties, against arbitrary number of corruptions, the best known protocol remained the protocol of [9]. We make a step towards eliminating the above gap, presenting a t-party, m-round coin-flipping protocol, with bias This improves upon the protocol of [9] for any t ≤ 1/2 · log log m, and in particular for t ∊ O(1), this yields an protocol. For the three-party case, this yields an protocol, improving over the the O(log3 m/m)-bias protocol of [32]. Our protocol generalizes that of [32], by presenting an appropriate “defense protocols” for the remaining parties to interact in, in the case that some parties abort or caught cheating ([32] only presented a two-party defense protocol, which limits their final protocol to handle three parties). We analyze our new protocols by presenting a new paradigm for analyzing fairness of coin-flipping protocols. We map the set of adversarial strategies that try to bias the honest parties outcome in the protocol to the set of the feasible solutions of a linear program. The gain each strategy achieves is the value of the corresponding solution. We then bound the the optimal value of the linear program by constructing a feasible solution to its dual.