We address one-time publishing of non-overlapping counts with ǫ-differential privacy. These statistics are useful in a wide and important range of applications, including transactional, traffic and medical data analysis. Prior work on the topic publishes such statistics with prohibitively low utility in several practical scenarios. Towards this end, we present GS, a method that pre-processes the counts by elaborately grouping and smoothing them via averaging. This step acts as a form of preliminary perturbation that diminishes sensitivity, and enables GS to achieve ǫ-differential privacy through low Laplace noise injection. The grouping strategy is dictated by a sampling mechanism, which minimizes the smoothing perturbation. We demonstrate the superiority of GS over its competitors, and confirm its practicality, via extensive experiments on real datasets.
For a set of non-overlapping counting queries, Kellaris and Papadopoulos et al. REF pre-processed the counts by elaborate grouping and smoothing them via averaging to reduce the sensitivity and thus the amount of injected noise.
Practical Differential Privacy via Grouping and Smoothing
PVLDB
PVLDB
Computer Science