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arXiv · 1511.08681

Algorithms for Differentially Private Multi-Armed Bandits

Aristide Tossou·Christos Dimitrakakis
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Abstract

We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that there exist $(ε, δ)$ differentially private variants of Upper Confidence Bound algorithms which have optimal regret, $O(ε^{-1} + \log T)$. This is a significant improvement over previous results, which only achieve poly-log regret $O(ε^{-2} \log^{2} T)$, because of our use of a novel interval-based mechanism. We also substantially improve the bounds of previous family of algorithms which use a continual release mechanism. Experiments clearly validate our theoretical bounds.

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BibTeXRIS

Aristide Tossou, Christos Dimitrakakis. 2015-11-27. Algorithms for Differentially Private Multi-Armed Bandits. https://arxiv.org/abs/1511.08681

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