Privacy-Preserving Bandits

By Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Benjamin Livshits. 2019

20 citations recorded by the source corpus.

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 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 $\epsilon \approx 0.693$. These results suggest P2B is an effective approach to challenges arising in on-device privacy-preserving personalization.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

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Citations in the corpus

Flower: A Friendly Federated Learning Research Framework (2020, background): “…has been tremendous progress in enabling the execution of deep learning models on mobile and embedded devices to infer user contexts and behaviors (Fromm et al., 2018; Chowdhery et al., 2019; Malekzadeh et al., 2019; Lee et al., 2019; Yao et al., 2019; LiKamWa et al., 2016; Georgiev et al., 2017).”