Grafting Laplace and Gaussian distributions: A new noise mechanism for differential privacy

12/19/2022

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by   Gokularam Muthukrishnan, et al.

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Indian Institute Of Technology, Madras

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The framework of Differential privacy protects an individual's privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best characteristics of both distributions. We theoretically analyse the proposed mechanism and we derive the necessary and sufficient condition in one dimension and a sufficient condition in high dimensions for the mechanism to guarantee (ϵ,δ)-differential privacy. Numerical simulations corroborate the efficacy of the proposed mechanism compared to other existing mechanisms in achieving better trade-off for privacy and accuracy.