Commoner Privacy And A Study On Network Traces
Xiyue Deng, Jelena Mirković · 2017
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.