Generalized Gaussian Mechanism for Differential Privacy
Generalized Gaussian Mechanism for Differential Privacy
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DOI：
10.1109/tkde.2018.2845388
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发表时间：
2019-04-01
影响因子：
8.9
通讯作者：
Liu, Fang
中科院分区：
文献类型：
--
作者：
Liu, Fang
在数据隐私研究和应用中，信息披露风险评估至关重要。差分隐私（DP）概念将隐私以概率形式形式化，并为隐私保护提供了一个健壮的概念。DP的实际应用包括开发DP机制，以便在预先指定的隐私预算下发布数据。本文基于统计查询的l(p)全局敏感性，将广泛应用的拉普拉斯机制推广到广义高斯机制族。探讨了GG机制在预设隐私参数下达到DP的理论要求，并基于GG分布分析了GG机制与指数机制之间的联系和区别。作为GG机制的一个特例，我们还给出了（epsilon, delta） dthorn-probabilistic DP的高斯机制尺度参数的下界，并比较了净化后的结果在高斯和拉普拉斯机制的尾部概率和色散方面的效用。最后，我们将GG机制应用于三个实验中，比较了净化后的结果在l(1)距离和Kullback-Leibler散度上的准确率，并检验了用净化后的数据构建的SVM分类器相对于原始结果的预测能力。
Assessment of disclosure risk is of paramount importance in data privacy research and applications. The concept of differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection. Practical applications of DP involve development of DP mechanisms to release data at a pre-specified privacy budget. In this paper, we generalize the widely used Laplace mechanism to the family of generalized Gaussian (GG) mechanism based on the l(p) global sensitivity of statistical queries. We explore the theoretical requirement for the GG mechanism to reach DP at prespecified privacy parameters, and investigate the connections and differences between the GG mechanism and the Exponential mechanism based on the GG distribution. We also present a lower bound on the scale parameter of the Gaussian mechanism of (epsilon, delta) dthorn-probabilistic DP as a special case of the GG mechanism, and compare the utility of sanitized results in the tail probability and dispersion between the Gaussian and Laplace mechanisms. Lastly, we apply the GG mechanism in three experiments and compare the accuracy of sanitized results in the l(1) distance and Kullback-Leibler divergence, and examine the prediction power of a SVM classifier constructed with the sanitized data relative to the original results.