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Differential Privacy with Higher Utility through Non-identical Additive Noise. (arXiv:2302.03511v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
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. …
conditions differential privacy distributed distribution guarantee higher noise non privacy study under utility work