One-Bit Matrix Completion with Differential Privacy

• URL: http://arxiv.org/abs/2110.00719v1
• Date: Sat, 2 Oct 2021 03:49:55 GMT
• Title: One-Bit Matrix Completion with Differential Privacy
• Authors: Zhengpin Li, Zheng Wei, Xiaojun Mao and Jian Wang
• Abstract summary: We propose a novel framework for one-bit matrix completion under the differential privacy constraint. Our proposed approaches can maintain high-level privacy with little loss of completion accuracy.
• Score: 6.409622409155275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Matrix completion is a prevailing collaborative filtering method for recommendation systems that requires the data offered by users to provide personalized service. However, due to insidious attacks and unexpected inference, the release of user data often raises serious privacy concerns. Most of the existing solutions focus on improving the privacy guarantee for general matrix completion. As a special case, in recommendation systems where the observations are binary, one-bit matrix completion covers a broad range of real-life situations. In this paper, we propose a novel framework for one-bit matrix completion under the differential privacy constraint. In this framework, we develop several perturbation mechanisms and analyze the privacy-accuracy trade-off offered by each mechanism. The experiments conducted on both synthetic and real-world datasets demonstrate that our proposed approaches can maintain high-level privacy with little loss of completion accuracy.

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