Privacy-Preserving Bandits [article]

Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits
2020 arXiv   pre-print
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. This paper proposes a technique we call
more » ... serving 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 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.
arXiv:1909.04421v4 fatcat:ynffzmb3czc33dxlkncocevyja