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Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
[article]
2019
arXiv
pre-print
Differential privacy (DP), provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data. We address the problem of releasing a noisy real-valued statistic vector T, a function of sensitive data under DP, via the class of K-norm mechanisms with the goal of minimizing the noise added to achieve privacy. First, we introduce the sensitivity space of T, which extends the concepts of sensitivity polytope and
arXiv:1801.09236v3
fatcat:ffab5ea5kreufgji6jcslh4aiq