Contextual bandit algorithms (CBAs) often rely on personal data to provide recommendations. This means that potentially sensitive data from past interactions are utilized to provide personalization to end-users. Using a local agent on the user’s device protects the user’s privacy, by keeping the data locally, 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 Privacy-Preserving Bandits (P2B), a system that updates local agents by collecting feedback from other 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 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 problems arising in on-device privacy-preserving personalization.
Mohammad Malekzadeh (Queen Mary University of London)
Dimitrios Athanasakis (Brave Software)
Hamed Haddadi (Brave Software)
Ben Livshits (Brave Software)
Related Events (a corresponding poster, oral, or spotlight)
2020 Poster: Privacy-Preserving Bandits »
Tue Mar 3rd 12:30 -- 03:00 AM Room Ballroom A