Differentially private (DP) databases can enable privacy-preserving analytics over datasets or data streams containing sensitive personal records. In such systems, user privacy is a very limited resource that is consumed by every new query, and hence must be aggressively conserved. We propose Boost, the most effective caching component for linear query workloads over DP databases. Boost builds upon private multiplicative weights (PMW), a DP mechanism that is powerful in theory but very ineffective in practice, and transforms it into a highly effective caching object, PMW-Bypass, which uses prior-query results obtained through an external DP mechanism to train a PMW to answer arbitrary future linear queries accurately and "for free" from a privacy perspective. We show that Boost with PMW-Bypass conserves significantly more budget compared to vanilla PMW and simpler cache designs: at least 1.51 - 14.25x improvement in experiments on public Covid19 and CitiBike datasets. Moreover, Boost incorporates support for range-query workloads, such as timeseries or streaming workloads, where opportunities exist to further conserve privacy budget through DP parallel composition and warm-starting of PMW state. Our work thus establishes both a coherent system design and the theoretical underpinnings for effective caching in DP databases.

翻译：差分隐私(DP)数据库能够在包含敏感个人记录的数据集或数据流上实现隐私保护分析。在这类系统中，用户隐私是极为有限的资源，每次新查询都会消耗该资源，因此必须采取激进策略予以节约。我们提出Boost方案——针对差分隐私数据库线性查询负载的最有效缓存组件。Boost基于私有乘法权重法(PMW)——一种理论强大但实际效用甚微的差分隐私机制——将其转化为高效缓存对象PMW-Bypass。该机制利用通过外部差分隐私机制获得的先前查询结果来训练PMW，从而在隐私视角下"零成本"精确回答任意未来线性查询。实验表明，在公共Covid19和CitiBike数据集上，采用PMW-Bypass的Boost方案相比原生PMW及简单缓存设计能显著节约预算：性能提升达1.51-14.25倍。此外，Boost还支持范围查询负载（如时间序列或流式负载），可通过差分隐私并行组合及PMW状态热启动进一步节约隐私预算。因此，我们的工作既建立了统一的系统设计，也为差分隐私数据库中的高效缓存奠定了理论基础。