Privacy-Preserving Edge Intelligence: A Perspective of Constrained Bandits

Advanced edge systems have brought intelligence to networked end devices at the network edge. In such systems, privacy preservation has been an integral role since users' privacy may be violated via edge-device interaction given unsafe decision-making on information sharing. Therefore, we in this paper study privacy preservation for decision-making under bandit models. Particularly, a canonical bandit model features an agent that aims to maximize attainable rewards based on feedback from arm selection. However, upon application in edge systems, such feedback becomes more complex given 1) privacy concern and 2) non-negligible cost feedback. Confronting such concerns during decision-making, we study a privacy-preserving constrained bandit variant where we face the challenge of guaranteeing privacy preservation and within-budget cost while striving for high rewards. In this paper, we address the challenge with an integration of local differential privacy mechanism, online control, and online learning. Theoretically, we prove that our algorithm maintains adjustable privacy, adheres to cost constraints, and achieves a sub-linear regret (i.e., loss of reward). Numerically, we conduct simulations to demonstrate the outperformance of our algorithm over baselines.