Two-Party Decision Tree Training from Updatable Order-Revealing Encryption

ePrint Report: Two-Party Decision Tree Training from Updatable Order-Revealing Encryption

Robin Berger, Felix Dörre, Alexander Koch

Running machine learning algorithms on encrypted data is a way forward to marry functionality needs common in industry with the important concerns for privacy when working with potentially sensitive data. While there is already a growing field on this topic and a variety of protocols, mostly employing fully homomorphic encryption or performing secure multiparty computation (MPC), we are the first to propose a protocol …

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