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Asymmetrical Vertical Federated Learning · Around

2020

Asymmetrical Vertical Federated Learning

Liu, Yang, Zhang, Xiong, Wang, Libin

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Understand

Federated learning is a distributed machine learning method that aims to preserve the privacy of sample features and labels.

In a federated learning system, ID-based sample alignment approaches are usually applied with few efforts made on the protection of ID privacy.
In real-life applications, however, the confidentiality of sample IDs, which are the strongest row identifiers, is also drawing much attention from many participants.
To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrical vertical federated learning and illustrates the way to protect sample IDs.

Built on

Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
Earlier work this paper cites.
Efficient private matching and set intersection
Michael J Freedman, Kobbi Nissim, and Benny Pinkas · 2004
Earlier work this paper cites.
Privacy-preserving inter-database operations
Gang Liang and Sudarshan S Chawathe · 2004
Earlier work this paper cites.

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Then

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