TY - JOUR
T1 - An Optimal Noise Mechanism for Cross-Correlated IoT Data Releasing
AU - Ou, Lu
AU - Qin, Zheng
AU - Liao, Shaolin
AU - Weng, Jian
AU - Jia, Xiaohua
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - cross correlations
KW - Data privacy
KW - Internet of Things
KW - optimization
KW - pufferfish privacy
KW - time-series data
UR - http://www.scopus.com/inward/record.url?scp=85112080588&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85112080588&origin=recordpage
U2 - 10.1109/TDSC.2020.3031223
DO - 10.1109/TDSC.2020.3031223
M3 - 21_Publication in refereed journal
VL - 18
SP - 1528
EP - 1540
JO - IEEE Transactions on Dependable and Secure Computing
JF - IEEE Transactions on Dependable and Secure Computing
SN - 1545-5971
IS - 4
M1 - 9224157
ER -