An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries

By Yehuda Lindell, Benny Pinkas. 2007

394 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Faster Secure Two-Party Computation in the Single-Execution Setting (2017, methodology): “The key idea is that the XOR of any i rows of A has Hamming weight at least i, contributed by Ik, so we do not need as much Hamming weight from the random matrix as in prior work [25].”
Obliv-C: A Language for Extensible Data-Oblivious Computation (2015, background): “Although there are even stronger protocols that provide complete privacy against malicious adversaries [8, 14, 17, 19, 24, 25, 28], they all require substantially more expensive techniques.”
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution (2012, methodology): “Several approaches have been proposed for achieving security against malicious adversaries [11, 22, 25, 33, 35, 27, 24, 32], some of which have been implemented [28, 34, 35, 32].”
GraphSC: Parallel Secure Computation Made Easy (2015, methodology): “Our focus is on the semi-honest model, although this can be extended with existing techniques [6], [68].”
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018, methodology): “, [22, 32, 14, 37]), and many tools have been developed for efficiently implementing MPC computations (e.”
Fast Cut-and-Choose-Based Protocols for Malicious and Covert Adversaries (2013, methodology): “In this paper, we present a novel twist on the cut-and-choose strategy used in [20,21,29,25] that enables us to achieve an error of just 2−s with s circuits (and some small additional overhead).”
Implementing AES via an Actively/Covertly Secure Dishonest-Majority MPC Protocol (2012, methodology): “To obtain security against active adversaries a variant of the cut-and-choose methodology of Lindell and Pinkas [20] was used, this resulted in the run-time dropping to 19 minutes to evaluate an AES encryption.”
Machine Learning Classification over Encrypted Data (2015, background): “Secure two-party computation protocols for generic functions exist in theory [4], [5], [22], [23], [24] and in practice [6], [7], [8].”
More Efficient Constant-Round Multi-Party Computation from BMR and SHE (2016, background): “Extraordinary efficiency has been achieved for both passive adversaries [4,1] and active adversaries [25,26,34,23,19,28,29].”
Private Set Intersection: Are Garbled Circuits Better than Custom Protocols? (2012, background): “, [21,28,30,37]) are available for converting protocols secure in the semi-honest setting to protocols secure under stronger notions of security (although the best known techniques still impose substantial cost).”
Billion-Gate Secure Computation with Malicious Adversaries (2012, methodology): “In addition to these original ideas, practical concerns involving large circuits and parallelization will be addressed.”
Zero-Knowledge Contingent Payments Revisited: Attacks and Payments for Services (2017, methodology): “Such protocols require the ability to extract the input during the simulation [36], so we use the simulator of the two-party protocol to extract r , s .”
Foundations of garbled circuits (2012, background): “5 for an example—consists of an Φ circ none NPS99 [46] privacy-preserving auctions prv Φ circ proxy OT KO04 [34] 2-party SFE (malicious) prv Φ circ OT, ZK proofs, commitment, trapdoor perm FAZ05 [19] private credit checking prv Φ size sym encryption FM06 [44] 2-party SFE (malicious) prv Φ circ OT, c”