Secure Best Arm Identification in the Presence of a Copycat
This addresses a security challenge in bandit algorithms for scenarios like online advertising or clinical trials where privacy is critical, representing a novel contribution beyond incremental improvements.
The paper tackles the problem of best arm identification in stochastic linear bandits while preventing a copycat observer from learning the best arm, proposing a secure algorithm that achieves an error exponent of Ω(T/log²(d)) with minimal information leakage.
Consider the problem of best arm identification with a security constraint. Specifically, assume a setup of stochastic linear bandits with $K$ arms of dimension $d$. In each arm pull, the player receives a reward that is the sum of the dot product of the arm with an unknown parameter vector and independent noise. The player's goal is to identify the best arm after $T$ arm pulls. Moreover, assume a copycat Chloe is observing the arm pulls. The player wishes to keep Chloe ignorant of the best arm. While a minimax--optimal algorithm identifies the best arm with an $Ω\left(\frac{T}{\log(d)}\right)$ error exponent, it easily reveals its best-arm estimate to an outside observer, as the best arms are played more frequently. A naive secure algorithm that plays all arms equally results in an $Ω\left(\frac{T}{d}\right)$ exponent. In this paper, we propose a secure algorithm that plays with \emph{coded arms}. The algorithm does not require any key or cryptographic primitives, yet achieves an $Ω\left(\frac{T}{\log^2(d)}\right)$ exponent while revealing almost no information on the best arm.