Billion-Gate Secure Computation with Malicious Adversaries

By Ben Kreuter, Abhi Shelat, Chih-Hao Shen. 2012

243 citations recorded by the source corpus.

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In Privacy & Security

Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, background): “, [48]) and some can handle dozens of parties (e.”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, methodology): “Closer to our setting, Kreuter et al. [31] designed a framework for two-party computation in the presence of a malicious garbler.”
Pinocchio: Nearly Practical Verifiable Computation (2013, background): “Secure multiparty computation, for example, has seen tremendous progress [57–59].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, neutral): “rt the corresponding limitations. PCF (Portable Circuit Format) [56] has introduced a compact representation of Boolean circuits that enables better scaling of secure computation programs. Authors in [57] have shown the evaluation of a circuit with billion gates in the malicious model by parallelizi”
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].”
More efficient oblivious transfer and extensions for faster secure computation (2013, unknown): “Note that by using the correlated input OT extension protocol, the server needs to garble the circuit after performing the OT extension; this order is also needed for the pipelining approach used in many implementations, e.g., [24,34,36].”
GraphSC: Parallel Secure Computation Made Easy (2015, background): “Although it was ﬁrst proposed about three decades ago [1], it is only in the last few years that the research community has made enormous progress at improving the efﬁciency of secure computation [2]–[6].”
Privacy-preserving matrix factorization (2013, background): “There are presently many frameworks that implement Yao garbled circuits [45, 23, 24, 44, 53, 25, 36].”
Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations (2015, unknown): “This can be thought of as using a number of garblerevaluator pairs, similarly to the cut-and-choose implementation of [24] for actively secure GC.”
Fast Garbling of Circuits Under Standard Assumptions (2015, methodology): “[18] who use AES-256 using AES-NI without pipelining, and to Huang et al.”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, background): “Though not the strongest model, this model forms the first step towards achieving protocols with stronger security guarantees [6, 29, 68, 71].”
Automated Synthesis of Optimized Circuits for Secure Computation (2015, methodology): “The compiler presented in [KSS12] also provides a domain speciﬁc input language and showed scalability to circuits consisting of billions of gates that were evaluated with a variant of Yao’s protocol with security against malicious adversaries.”
Obliv-C: A Language for Extensible Data-Oblivious Computation (2015, methodology): “includes using AES-NI instructions [1, 16] to garbled each gate, or OT-extension for malicious adversaries [13] (our current dual execution implementation does not use OT-extension).”
Efficient Garbling from a Fixed-Key Blockcipher (2013, methodology): “Taking RSA-32 as an example, KSS report a garbling time of 4.53 seconds, which translates to 6546 cpg, while JustGarble uses 91.6 cpg, a 70x speedup.”
MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (2016, background): “In the last decade, MPC has rapidly moved from purely theoretical study to an object of practical interest, with a growing interest in practical applications, and many implementations now capable of handling complex computations [28, 29].”
Secure Computation on Floating Point Numbers (2013, methodology): “Recent implementations of secure multi-party techniques include [6], [5] and two-party techniques include [31], [33].”