Differential Privacy in the Extensive-Form Bandit Problem
We consider the extensive-form bandit problem, where on each trial the learner (a user coordinated by a server) plays an extensive-form game against an oblivious adversary, observing the information sets it finds itself in as well as the resulting payoff/loss. We give an algorithm for this problem that satisfies $ε$-local differential privacy and attains a regret of $\tilde{O}(\sqrt{A\ln(S)T}/ε)$, where $A$ is the total number of actions that the learner can possibly take, $S$ is the number of the learner's possible reduced strategies, and $T$ is the number of trials. On each trial, the time complexity of our algorithm is, up to a factor logarithmic in the maximum number of actions at an ...
| Authors | Stephen Pasteris, Rahul Savani, Theodore Turocy |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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