Privacy-Preserving Bandits

Part of Proceedings of Machine Learning and Systems 2 (MLSys 2020)

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Authors

Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Ben Livshits

Abstract

<p>Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's privacy, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users.</p> <p>This paper proposes a technique we call Privacy-Preserving Bandits (P2B); a system that updates local agents by collecting feedback from other local agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed only a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget ε ≈ 0.693. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization. </p>