Privacy-Preserving Distributed Estimation via Laplacian and Gaussian Differential-Privacy Noise

Abstract In this paper, a private distributed estimation algorithm is proposed. In this algorithm, a differential-privacy noise is added to the intermediate estimation to be exchanged among nodes. Two types of differential noise is regarded in the paper which are Gaussian and Laplacian. Also, in each case, two approaches are used to recover the true intermediate estimations. In the first approach, we estimate the true intermediate estimation and in the second approach, we estimate the noise vector and then subtract it from the noise intermediate estimation. We show that both approaches lead to the same formula for denoised intermediate estimation. Simulation experiments corroborate the effectiveness of the proposed algorithm when variance of added privacy noise is high and the privacy is guaranteed with high confidence.