Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism can Beat Gaussian in High Dimensions

arXiv:2302.03511v2 Announce Type: replace
Abstract: Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise which is independent, but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy-accuracy trade-off by exploiting coordinate-wise disparity. 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 …

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