We propose a new approach to practical two-party computation secure against an active adversary. All prior practical protocols were based on Yao's garbled circuits. We use an OT-based approach and get efficiency via OT extension in the random oracle model. To get a practical protocol we introduce a number of novel techniques for relating the outputs and inputs of OTs in a larger construction. We also report on an implementation of this approach, that shows that our protocol is more efficient than any previous one: For big enough circuits, we can evaluate more than 20000 Boolean gates per second. As an example, evaluating one oblivious AES encryption (∼ 34000 gates) takes 64 seconds, but when repeating the task 27 times it only takes less than 3 seconds per instance.
Abstract. An additively-homomorphic encryption scheme enables us to compute linear functions of an encrypted input by manipulating only the ciphertexts. We define the relaxed notion of a semihomomorphic encryption scheme, where the plaintext can be recovered as long as the computed function does not increase the size of the input "too much". We show that a number of existing cryptosystems are captured by our relaxed notion. In particular, we give examples of semi-homomorphic encryption schemes based on lattices, subset sum and factoring. We then demonstrate how semi-homomorphic encryption schemes allow us to construct an efficient multiparty computation protocol for arithmetic circuits, UC-secure against a dishonest majority. The protocol consists of a preprocessing phase and an online phase. Neither the inputs nor the function to be computed have to be known during preprocessing. Moreover, the online phase is extremely efficient as it requires no cryptographic operations: the parties only need to exchange additive shares and verify information theoretic MACs. Our contribution is therefore twofold: from a theoretical point of view, we can base multiparty computation on a variety of different assumptions, while on the practical side we offer a protocol with better efficiency than any previous solution.
Oblivious Transfer (OT) is one of the fundamental building blocks of cryptographic protocols. In this paper we describe the simplest and most efficient protocol for 1-out-of-n OT to date, which is obtained by tweaking the Diffie-Hellman key-exchange protocol. The protocol allows to perform m 1-out-of-n OTs using only 2 + 3m full exponentiations (2m for the receiver, 2 + m for the sender) and, sending only m + 1 group elements and 2mn ciphertexts. We also report on an implementation of the protocol using elliptic curves, and on a number of mechanisms we employ to ensure that our software is secure against active attacks too. Experimental results show that our protocol (thanks to both algorithmic and implementation optimizations) is at least one order of magnitude faster than previous work.
Mechanism design deals with distributed algorithms that are executed with self-interested agents. The designer, whose objective is to optimize some function of the agents private types, needs to construct a computation that takes into account agent incentives which are not necessarily in alignment with the objective of the mechanism. Traditionally, mechanisms are designed for agents who only care about the utility they derive from the mechanism outcome, which often fully or partially discloses their (declared) types. Such mechanisms may become inadequate when agents are privacy-aware, i.e., when their loss of privacy adversely affects their utility. In such cases ignoring privacy-awareness in the design of a mechanism may render it not incentive compatible, and hence inefficient. Interestingly, and somewhat counter-intuitively, Xiao [eprint 2011] has recently showed that this can happen even when the mechanism preserves a strong notion of privacy.Towards constructing mechanisms for privacy-aware agents, we put forward and justify a model of privacy-aware mechanism design. We then show that privacy-aware mechanisms are feasible. The following is a summary of our contributions:-Modeling privacy-aware agents: We propose a new model of privacy-aware agents where agents need only have a conservative upper bound on how loss of privacy adversely affects their utility. This is in deviation from prior modeling which required full characterization. -Privacy of the privacy loss valuations: Agent privacy valuations are often sensitive on their own. Our model of privacy-aware mechanisms takes into account the loss of utility due to information leaked about these valuations. -Guarantees for agents with high privacy valuations: As it is impossible to guarantee incentive compatibility for agents that have arbitrarily high privacy valuations, we require a privacy-aware mechanism to set a threshold such that the mechanism is incentive compatible w.r.t. agents whose privacy valuations are below the threshold, and differential privacy is guaranteed for all other agents. -Constructing privacy-aware mechanisms: We first construct a privacy-aware mechanism for a simple polling problem, and then give a more general result, based on recent generic construction of approximately additive mechanisms by Nissim, Smorodinsky, and Tennenholtz [ITCS 2012]. We show that under a mild assumption on the distribution of privacy valuations (namely, that valuations are bounded for all but a vanishing fraction of the population) these constructions are incentive compatible w.r.t. almost all agents, and hence give an approximation of the optimum. Finally, we show how to apply our generic construction to get a mechanism for privacy-aware selling of digital goods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.