Locally Differentially Private (Contextual) Bandits Learning
We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free bandits learning problems with LDP guarantee. Based on our frameworks, we can improve previous best results for private bandits learning with one-point feedback, such as private Bandits Convex Optimization, and obtain the first result for Bandits Convex Optimization (BCO) with multi-point feedback under LDP. LDP
... tee and black-box nature make our frameworks more attractive in real applications compared with previous specifically designed and relatively weaker differentially private (DP) context-free bandits algorithms. Further, we extend our (ε, δ)-LDP algorithm to Generalized Linear Bandits, which enjoys a sub-linear regret Õ(T^3/4/ε) and is conjectured to be nearly optimal. Note that given the existing Ω(T) lower bound for DP contextual linear bandits (Shariff Sheffe, 2018), our result shows a fundamental difference between LDP and DP contextual bandits learning.