Prochlo: Strong Privacy for Analytics in the Crowd
About
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)
Andrea Bittau, \'Ulfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Usharsee Kode, Julien Tinnes, Bernhard Seefeld• 2017
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Forecasting | ComStock (val) | RMSE0.386 | 18 | |
| Load forecasting | London Household Electricity (val) | RMSE0.418 | 18 | |
| Load forecasting | Pecan Street (val) | RMSE0.377 | 18 | |
| Sample Inference Attack | CIFAR-10 shadow dataset (test) | Original SIA Success Rate45 | 4 | |
| Privacy Leakage Evaluation | CIFAR-10 | Cosine AUC0.36 | 3 |
Showing 5 of 5 rows