Turbo: Effective Caching in Differentially-Private Databases

TL;DR

Turbo introduces a caching layer for differentially-private databases that significantly improves privacy budget efficiency for linear query workloads by transforming PMW into an effective cache mechanism, enabling more accurate answers with less privacy cost.

Contribution

The paper presents Turbo, a novel caching system that enhances the practical effectiveness of PMW in DP databases, enabling more accurate and privacy-efficient query answering.

Findings

Turbo conserves 1.7-15.9x more privacy budget compared to vanilla PMW.

Supports range query workloads like timeseries and streams.

Provides a theoretical foundation for caching in DP systems.

Abstract

Differentially-private (DP) databases allow for privacy-preserving analytics over sensitive datasets or data streams. In these systems, user privacy is a limited resource that must be conserved with each query. We propose Turbo, a novel, state-of-the-art caching layer for linear query workloads over DP databases. Turbo builds upon private multiplicative weights (PMW), a DP mechanism that is powerful in theory but ineffective in practice, and transforms it into a highly-effective caching mechanism, PMW-Bypass, that 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. Our experiments on public Covid19 and CitiBike datasets show that Turbo with PMW-Bypass conserves 1.7-15.9x more budget compared to vanilla PMW and simpler cache designs, a significant improvement. Moreover, Turbo provides support for range query workloads, such as timeseries or streams, where opportunities exist to further conserve privacy budget through DP parallel composition and warm-starting of PMW state. Our work provides a theoretical foundation and general system design for effective caching in DP databases.