论文标题
如果可以的话，请缓存我：差异私人数据探索的准确性推理引擎
Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration
论文作者
论文摘要
差异隐私（DP）允许数据分析师查询包含用户敏感信息的数据库，同时为用户提供可量化的隐私保证。最新的交互式DP系统（例如APEX）提供了对查询响应的准确性保证，但由于与过去的查询独立处理传入的查询，因此无法支持大量查询，其总隐私预算有限。我们提出了一种交互式，准确感知的DP查询引擎CachedP，它利用了过去的响应的差异私有缓存，以较低的隐私预算来回答当前的工作量，同时满足严格的准确性保证。我们通过新型的高速缓存DP成本优化将复杂的DP机制与结构化缓存整合在一起。我们的全面评估表明，与相关工作相比，CachedP可以准确地回答各种工作量序列，同时降低隐私损失。
Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses, but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries. We present an interactive, accuracy-aware DP query engine, CacheDP, which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budget, while meeting strict accuracy guarantees. We integrate complex DP mechanisms with our structured cache, through novel cache-aware DP cost optimization. Our thorough evaluation illustrates that CacheDP can accurately answer various workload sequences, while lowering the privacy loss as compared to related work.
