MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer

By Marcel Keller, E. Orsini, Peter Scholl. 2016

497 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Several generic techniques have been developed for MPC including Yao’s garbled circuits protocol [70], the GMW protocol [31], and other secret-sharing based techniques [7, 41, 50].”
SPD Z 2 k : Eﬃcient MPC mod 2 k for Dishonest Majority (2018, methodology): “Note that like MASCOT [14] — the most practical OT-based protocol for actively secure, dishonest majority MPC over ﬁnite ﬁelds — we expect that communication will be the bottleneck, since the protocol has very simple computational costs.”
Secure Multiparty Sorting Protocols with Covert Privacy (2016, methodology): “This precomputation used to be several orders of magnitude more expensive than the actual execution of the protocol, but recent advances have brought down the cost [21].”
Generalizing the SPDZ Compiler For Other Protocols (2018, methodology): “8xlarge instances due to the larger memory requirement of homomorphic encryption, while the MASCOT protocol [19] has been used for BMR.”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “When the input and output of the hash are a single 128-bit block, the hash function can be done using ﬁxed-key AES (for a random, pre-agreed key) with the simple construction : Note that this optimization was previously used for MPC in [10].”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “The state-of-the-art MASCOT protocol [28] achieves a rate of below 1,000 multiplication gates (for 3 parties), which is orders of magnitude slower than what can be achieved in the honest-majority setting.”
High-Throughput Secure Three-Party Computation for Malicious Adversaries and an Honest Majority (2017, background): “Likewise, the SPDZ/MASCOT protocol [14] communicates approximately 360 bits per gate for three parties, which is 36 times the cost of our protocol.”
Global-Scale Secure Multiparty Computation (2017, background): “• SPDZ-style [19]: Each party holds a share of a global MAC For a secret bit x , each party holds a share of x and a share of the MAC on x .”
ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation (2020, methodology): “We now provide the details for instantiating setupMULT using two of the well-known primitives: i) Oblivious Transfer (OT) as used in [39, 59] and ii) Homomorphic Encryption (HE) as used in [38,49,90].”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “CRYPTEN adopts a trusted third party for generating Beaver triples for efficiency reasons, but we are planning to add TTP-free solutions based on additive homomorphic encryption [52] or oblivious transfer [39].”
SPDℤ2k: Efficient MPC mod 2k for Dishonest Majority (2018, unknown): “7, is similar to that in other secret sharing-based protocols like GMW, BeDOZa, SPDZ and MASCOT [2,9,11,14].”