Abstract

Differential privacy has emerged as a promising mechanism for privacy-safe data mining. One popular differential privacy mechanism allows researchers to pose queries over a dataset, and adds random noise to all output points to protect privacy. While differential privacy produces useful data in many scenarios, added noise may jeopardize utility for queries posed over small populations or over long-tailed datasets. Gehrke et al. proposed crowd-blending privacy, with random noise added only to those output points where fewer than k individuals (a configurable parameter) contribute to the point in the same manner. This approach has a lower privacy guarantee, but preserves more research utility than differential privacy.
We propose an even more liberal privacy goal---commoner privacy---which fuzzes (omits, aggregates or adds noise to) only those output points where an individual's contribution to this point is an …

Date

December 4, 2017

Authors

Xiyue Deng, Jelena Mirkovic

Book

Proceedings of the 33rd Annual Computer Security Applications Conference

Pages

566-576