Global-Scale Secure Multiparty Computation

By Xiao Shaun Wang, Samuel Ranellucci, Jonathan Katz. 2017

224 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Honeycrisp: large-scale differentially private aggregation without a trusted core (2019, methodology): “, [3, 16, 73]) make it seem likely that this cost can be further reduced in the coming years.”
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, background): “Recent years witnessed a large body of works on improving the practical efficiency of MPC, and state-of-the-art protocols have achieved orders of magnitude improvements on performance (see e.g. [20, 36, 49]).”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, background): “[43], and binary computation based on secret sharing [36, 75].”
Efficient Pseudorandom Correlation Generators: Silent OT Extension and More (2019, background): “circuits [10, 29] and binary circuits [59, 61, 62], use this kind of corruptible, correlated randomness, since it is often easier to design a protocol that realizes this.”
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018, background): “It is now practical to execute two-party protocols with millions of inputs [21, 23], and global-scale, many-party protocols with malicious level security for small inputs [54].”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, methodology): “The toolkit includes three maliciously secure protocols we did not study: a two-party computation that checks input validity [88], a two-party computation library [123], and a multi-party protocol [125].”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, background): “we describe their protocol in the two-party setting but many (not all) of the techniques generalize readily to the multi-party setting (Wang et al., 2017c).”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “However, binary triples [ x ] 2 , [ y ] 2 , [ z ] 2 can be generated much more efﬁciently by exploiting TinyOT-style protocols [7], [36], [25], which generate triples with XOR-shared MACs and shares, as we now show.”