An Optimal Noise Mechanism for Cross-Correlated IoT Data Releasing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number9224157
Pages (from-to)1528-1540
Journal / PublicationIEEE Transactions on Dependable and Secure Computing
Volume18
Issue number4
Online published14 Oct 2020
Publication statusPublished - Jul 2021

Abstract

Cross correlations are ubiquitous in time-series IoT data sets such as trajectories from smartphones and smart meters data in smart grids. Conventional privacy methods have difficulty to protect cross correlation privacy within such correlated data set. Here we propose a novel Correlated noise mechanism for Cross-correlated Data Privacy (CCDP). Because the Fourier coefficients of the cross correlation of two data records are the linear product of those of the two data records, the sanitizing Fourier coefficients noise is used for efficient optimization. Also, the noise is added via the Geometric sum method, which is proved to provide the required Laplace distribution. We perform rigorous mathematical analysis of the CCDP and prove that it satisfies epsilon-Pufferfish privacy. We also prove that the CCDP can achieve the optimal data utility for a given privacy budget epsilon. What's more important, we further derive the mathematical procedure to obtain the optimal Laplace noise scale parameter to achieve better data utility. Simulations show that the proposed CCDP outperforms the independent Fourier coefficients noise mechanism, as well as two other state-of-the-art time-domain privacy mechanisms in the literature, for three types of data sets: computer-generated data, real-world trajectory data, and smart meter data.

Research Area(s)

  • cross correlations, Data privacy, Internet of Things, optimization, pufferfish privacy, time-series data

Citation Format(s)

An Optimal Noise Mechanism for Cross-Correlated IoT Data Releasing. / Ou, Lu; Qin, Zheng; Liao, Shaolin; Weng, Jian; Jia, Xiaohua.

In: IEEE Transactions on Dependable and Secure Computing, Vol. 18, No. 4, 9224157, 07.2021, p. 1528-1540.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review