Privacy-preserving machine learning based on secure two-party computations

The paper is devoted to the analysis of privacy-preserving machine learning systems based on secure two-party computations. The paper provides introductory information about privacy-preserving machine learning systems, analyses the goals and objectives of its application. A generalized model of priv...

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Hlavní autoři:	Sergey V. Zapechnikov, Andrey Yu. Shcherbakov
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Joint Stock Company "Experimental Scientific and Production Association SPELS 2021-12-01
Edice:	Безопасность информационных технологий
Témata:	privacy-preserving machine learning, secure multi-party computations, garbled circuits, oblivious transfer, secret sharing scheme, homomorphic encryption.
On-line přístup:	https://bit.mephi.ru/index.php/bit/article/view/1374
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