Achieving Privacy in the Adversarial Multi-Armed Bandit

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

Machine Learning Methods

In this paper, we improve the previously best known regret Êbound to achieve _-differential privacy in oblivious adversarial Êbandits from O(T2/3 /_) to O(ÃT lnT/_). This is achieved Êby combining a Laplace Mechanism with EXP3. We show that though EXP3 is already differentially private, it leaks a linear Êamount of information in T. However, we can improve this Êprivacy by relying on its intrinsic exponential mechanism for selecting actions. This allows us to reach O(Ã ln T)-DP, with a a regret of O(T2/3) that holds against an adaptive adversary, an improvement from the best known of O(T3/4). This is done by using an algorithm that run EXP3 in a mini-batch loop. Finally, we run experiments that clearly demonstrate the validity of our theoretical analysis.

10.1609/aaai.v31i1.10896