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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Bibliografiska uppgifter

Huvudupphov:	Sergey V. Zapechnikov, Andrey Yu. Shcherbakov
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Joint Stock Company "Experimental Scientific and Production Association SPELS 2021-12-01
Serie:	Безопасность информационных технологий
Ämnen:	privacy-preserving machine learning, secure multi-party computations, garbled circuits, oblivious transfer, secret sharing scheme, homomorphic encryption.
Länkar:	https://bit.mephi.ru/index.php/bit/article/view/1374
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