Faster Secure Two-Party Computation Using Garbled Circuits

By Yan Huang, David E. Evans, Jonathan Katz, Lior Malka. 2011

682 citations recorded by the source corpus.

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Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Popular applications used for the empirical evaluation of practical MPC systems include graph algorithms [5, 32, 42, 51, 57], data structure algorithms [42, 51], string matching and distance algorithms [36, 37, 66] and AES [3, 36, 37, 66].”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, methodology): “We built our system on top of FastGC [6], a Java-based open-source framework that enables developers to define arbitrary circuits using elementary XOR, OR and AND gates.”
Pinocchio: Nearly Practical Verifiable Computation (2013, background): “Secure multiparty computation, for example, has seen tremendous progress [57–59].”
VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, background): “The computation can also be shared between independent parties while guaranteeing confidentiality for individual inputs (using e. g., garbled circuits [29]) and providing protection against corrupted parties (see e. g., SPDZ [19]).”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “m in a high-level language called Secure Function Definition Language (SFDL) which is translated into a Boolean circuit. FariplayMP [15] is the extension of Fairplay to the multiparty setting. FastGC [51] reduces the running time and memory requirements of the GC execution by using pipelining. TinyG”
Taking Proof-Based Verified Computation a Few Steps Closer to Practicality (2012, background): “Homomorphic encryption (which enables computation over ciphertext) and secure multi-party protocols (in which participants compute over private data, revealing only the result [34, 40, 56]) provide only privacy guarantees, but one can build on them for verifiable computation.”
Verifying computations without reexecuting them (2015, background): “2 Thus, we do not treat exciting work on efﬁcient implementations of secure multi-party protocols [28, 31].”
Secure Multilayer Perceptron Based On Homomorphic Encryption (2018, background): “We consider them as honest but curious [16].”
TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits (2015, background): “While GC was considered to be prohibitively expensive and practically infeasible a decade ago, today we are witnessing a surge of theoretical, algorithmic, and tool developments that have significantly improved the efficiency and practicality of the GC protocol, see [2], [36], [44], [54], [60].”
A Hybrid Architecture for Interactive Verifiable Computation (2013, methodology): “Although some of this is intrinsic, we believe that much of the cost is a function of an unoptimized implementation; optimization plus techniques described elsewhere [26, 30] should lead to significant improvements.”
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution (2012, methodology): “Our implementations are based on the framework of Huang et al. [16, 15].”
Optimized Honest-Majority MPC for Malicious Adversaries — Breaking the 1 Billion-Gate Per Second Barrier (2017, unknown): “See [15], [18], [8], [14], [9], [19], [4], [17], [24], [20], [26], [16], [23] for just some examples.”
Software Grand Exposure: SGX Cache Attacks Are Practical (2017, background): “, fully homomorphic encryption [21] and secure multi-party computation [26]) are, for most applications, too slow.”
Privacy-preserving matrix factorization (2013, methodology): “Our garbled circuit construction was based on FastGC, a publicly available garbled circuit framework [24].”
Faster Secure Two-Party Computation in the Single-Execution Setting (2017, background): “[16] to reduce memory usage and hence improve efficiency.”
Implementing AES via an Actively/Covertly Secure Dishonest-Majority MPC Protocol (2012, background): “Huang et al [16] improve this even further obtaining a time of 0.2 seconds per block for semi-honest adversaries.”
Billion-Gate Secure Computation with Malicious Adversaries (2012, methodology): “Goyal, Mohassel, and Smith [11] therefore pointed out an insight that the evaluator could examine the correctness of those check circuits by receiving a hash of the garbled circuit first, acquiring the random seed, and reconstructing the circuit and hash by herself.”