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Generalized Gaussian Mechanism for Differential Privacy

机译:广义高斯差分隐私机制

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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.
机译:在数据隐私研究和应用中,评估披露风险至关重要。差异隐私(DP)的概念以概率的形式形式化了隐私,并提供了可靠的隐私保护概念。 DP的实际应用涉及DP机制的开发,以便以预先指定的隐私预算释放数据。在本文中,我们基于统计查询的l(p)全局敏感性,将广泛使用的Laplace机制推广到广义高斯(GG)机制族。我们探讨了GG机制在预先指定的隐私参数下达到DP的理论要求,并研究了基于GG分布的GG机制与指数机制之间的联系和区别。我们还提出了(epsilon,delta)棘刺概率DP的高斯机理的尺度参数的下界,作为GG机理的特例,并比较了经过消毒处理的结果在高斯概率和高斯概率之间的尾部概率和离散度中的效用拉普拉斯机制。最后,我们在三个实验中应用了GG机制,并比较了l(1)距离和Kullback-Leibler散度中净化结果的准确性,并检验了使用净化数据构造的SVM分类器相对于原始结果的预测能力。

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