Prochlo: Strong Privacy for Analytics in the Crowd

TL;DR

Prochlo introduces a privacy-preserving system architecture for large-scale user monitoring that balances data utility with strong privacy protections, based on practical deployment experiences.

Contribution

The paper presents the ESA architecture and Prochlo implementation, offering a novel approach to privacy-preserving analytics in large-scale monitoring systems.

Findings

Effective privacy protection in large-scale user monitoring

High utility data collection with privacy guarantees

Practical deployment demonstrates system viability

Abstract

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)