在安全约束下，如何平衡隐私保护与算法性能。
The Safety-Privacy Tradeoff in Linear Bandits
- 设计兼顾安全与隐私的线性多臂老虎机算法
- 隐私越高，安全风险越大，后悔值上升
- 适用于数据敏感且需安全控制的决策场景
我们研究一类线性随机多臂老虎机问题，每个问题建模不同代理对干预措施的随机响应，由全局安全约束连接。中心协调者需在每轮选择动作以最小化累积后悔，同时确保所有代理的期望响应满足全局安全约束，尽管对各老虎机参数存在不确定性。代理视其观测响应为私密信息，因此数据共享采用局部差分隐私（LDP）机制。然而，提高隐私水平会带来安全与后悔的代价。本文通过引入安全集的尖锐性（sharpness）概念——衡量安全集几何特性对后悔增长的影响——构建了在给定最大后悔预算下，各代理不可再改进的最优隐私级别向量。
原文摘要 · Abstract (English)
We consider a collection of linear stochastic bandit problems, each modeling the random response of different agents to proposed interventions, coupled together by a global safety constraint. We assume a central coordinator must choose actions to play on each bandit with the objective of regret minimization, while also ensuring that the expected response of all agents satisfies the global safety constraints at each round, in spite of uncertainty about the bandits' parameters. The agents consider their observed responses to be private and in order to protect their sensitive information, the data sharing with the central coordinator is performed under local differential privacy (LDP). However, providing higher level of privacy to different agents would have consequences in terms of safety and regret. We formalize these tradeoffs by building on the notion of the sharpness of the safety set - a measure of how the geometric properties of the safe set affects the growth of regret - and propose a unilaterally unimprovable vector of privacy levels for different agents given a maximum regret budget.
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