From Keys to Databases - Real-World Applications of Secure Multi-Party Computation

By David W. Archer, Dan Bogdanov, Yehuda Lindell, Liina Kamm, K. Nielsen, J. Pagter, Nigel P. Smart, Rebecca N. Wright. 2018

154 citations recorded by the source corpus.

. We discuss the widely increasing range of applications of a cryptographic technique called Multi-Party Computation. For many decades this was perceived to be of purely theoretical interest, but now it has started to ﬁnd application in a number of use cases. We highlight in this paper a number of these, ranging from securing small high value items such as cryptographic keys, through to securing an entire database.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “End-to-end privacy requires seamless integration between data-processing frameworks, such as secure SQL implementations [5], and data-modeling frameworks like CRYPTEN.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, methodology): “The SCALE-MAMBA systems use a set of bytecodes as an intermediate representation that have been reused in other projects, such as the Jana compiler [5].”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, background): “While MPC has been shown to be practical [7]– [9], MPC for a small number of parties in the honest majority setting [10]–[15], [15]–[19] has become popular over the last few years due to applications such as financial data analysis [20], email spam filtering [21], distributed credential encryption [”