Convolutions in Overdrive: Maliciously Secure Convolutions for MPC

ePrint Report: Convolutions in Overdrive: Maliciously Secure Convolutions for MPC

Marc Rivinius, Pascal Reisert, Sebastian Hasler, Ralf Kuesters

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are …

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