Differential privacy is typically ensured by perturbation with additive noise that is sampled from a known distribution. Conventionally, independent and identically distributed (i.i.d.) noise samples are added to each coordinate. In this work, propose to add noise which is independent, but not identically distributed (i.n.i.d.) across the coordinates. In particular, we study the i.n.i.d. Gaussian and Laplace mechanisms and obtain the conditions under which these mechanisms guarantee privacy. The optimal choice of parameters that ensure these conditions are derived theoretically. Theoretical analyses and numerical simulations show that the i.n.i.d. mechanisms achieve higher utility for the given privacy requirements compared to their i.i.d. counterparts.
翻译:通过从已知分布中取样的添加噪音的干扰,通常可以确保不同的隐私。在每一个坐标上添加了《公约》、独立和同样分布的(即d)噪音样本。在这项工作中,建议增加独立但并非相同分布的(即n.i.d.)在坐标上方的噪音。特别是,我们研究i.n.i.d.Gaussian和Laplace机制,并获得这些机制保障隐私的条件。最佳选择参数以确保从理论上推断出这些条件。理论分析和数字模拟表明,i.n.i.d.机制比i.d.机制对特定隐私要求的效用更高。