ReBound：用于交互式决策支持的重用感知隐私
ReBound: Reuse-Aware Privacy For Interactive Decision Support
AI总结：
研究交互式决策支持中差分隐私框架独立处理查询的问题，提出ReBound框架，通过重用缓存结果、引入重用框架、缓存图结构及协商机制，降低隐私成本并保证效用。
AI中文摘要：
差分隐私决策支持框架能以假阴性和假阳性率的形式界限回答复杂聚合阈值查询，但独立处理每个查询，不记忆过去结果。实际上分析师会交互式工作，发出相关查询序列。我们提出ReBound框架，它能重用先前查询的缓存结果，以降低或零额外隐私成本回答新查询，同时保持形式效用保证。该框架引入多种细化类型的重用框架、高效查找可重用结果的缓存图结构以及预算内无法满足请求界限时的协商机制。
英文摘要：
Differentially private decision support frameworks answer complex aggregate threshold queries with formal bounds on false negative and false positive rates, but treat each query independently with no memory of past results. In practice, analysts work interactively, issuing sequences of related queries that refine bounds, adjust thresholds, or derive new functions from previous ones. We propose ReBound, a framework that reuses cached results from previous queries to answer new queries at reduced or zero additional privacy cost while maintaining formal utility guarantees. ReBound introduces a reuse framework for multiple refinement types, a cache graph structure for efficient lookup of reusable results, and a negotiation mechanism for when requested bounds cannot be met within budget.
