在隐私保护下高效识别高收益选项，兼顾安全与效果。
Locally Differentially Private Thresholding Bandits
- 用伯努利机制生成私密反馈，保护用户数据
- 理论证明算法性能接近隐私约束下的最优水平
- 适合需要严格隐私保障的在线决策场景
本文研究在阈值赌博机问题中引入局部差分隐私的影响。考虑固定预算和固定置信度两种设置，提出利用基于伯努利的差分隐私机制获得的私密响应，来识别期望收益超过预设阈值的臂。该方法提供强隐私保障，并推导出所提算法的理论性能边界。此外，给出一般性下界，刻画任何差分隐私机制带来的额外损失，并证明所提算法在多对数因子内达到这些下界。结果为赌博机问题中的隐私保护决策框架提供了重要洞察。
原文摘要 · Abstract (English)
This work investigates the impact of ensuring local differential privacy in the thresholding bandit problem. We consider both the fixed budget and fixed confidence settings. We propose methods that utilize private responses, obtained through a Bernoulli-based differentially private mechanism, to identify arms with expected rewards exceeding a predefined threshold. We show that this procedure provides strong privacy guarantees and derive theoretical performance bounds on the proposed algorithms. Additionally, we present general lower bounds that characterize the additional loss incurred by any differentially private mechanism, and show that the presented algorithms match these lower bounds up to poly-logarithmic factors. Our results provide valuable insights into privacy-preserving decision-making frameworks in bandit problems.
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