Secure Multi-party Computation

By Zhang Wen-k. 2014

372 citations recorded by the source corpus.

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Citations in the corpus

Privacy-preserving outsourced classification in cloud computing (2017, background): “Most of the privacy-preserving techniques in machine learning focus on secure multiparty computation (SMC) [21], (fully) homomorphic encryption [20,22], garbled circuits (GCs) [23] and so forth.”
Balancing privacy versus accuracy in research protocols (2015, background): “Special and more convoluted computations can secretly carry out operations beyond just taking averages ( 9 ).”
Hiding in the Crowd: A Massively Distributed Algorithm for Private Averaging with Malicious Adversaries (2018, methodology): “We consider two commonly adopted adversary models for users, which were formalized by Goldreich (1998) and are used in the design of many secure protocols.”
Data Matching (2012, methodology): “In the second security model, known as the “malicious” model [123, 134], it is assumed that the parties that take part in a protocol do not necessarily follow the steps of the protocol, but they might maliciously communicate either wrong or modified data in order to be able to infer any sensitive in”
Universally Composable Two-Party and Multi-party Secure Computation (unknown): “In the non-adaptive case the protocol of [25, 33, 31] suÆces.”
Distributed Differentially Private Averaging with Improved Utility and Robustness to Malicious Parties (2020, methodology): “We consider two commonly adopted adversary models, which were formalized by [42] and are used in the design of many secure protocols.”