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)
@article{arxiv.1710.00901,
title = {Prochlo: Strong Privacy for Analytics in the Crowd},
author = {Andrea Bittau and Úlfar Erlingsson and Petros Maniatis and Ilya Mironov and Ananth Raghunathan and David Lie and Mitch Rudominer and Usharsee Kode and Julien Tinnes and Bernhard Seefeld},
journal= {arXiv preprint arXiv:1710.00901},
year = {2018}
}