Singular Spectrum Analysis for Local Differential Privacy of Classifications in the Smart Grid

Abstract

New privacy implications are induced to individuals and families because of the time-series data classification problem in the Internet of Things such as appliance classifications in the smart grid. To prevent the adversary from inferring the household appliance classification used in the smart grid, a singular spectrum analysis (SSA) has been applied to the local differential privacy (SSA-LDP). First, the Fourier spectrum noise has been added via the geometric sum which has been proved to achieve the Laplace noise distribution. Furthermore, we have proved that the sanitized data through the SSA-LDP is ε -deferentially private for the adversary inference attack. In addition, to achieve a better data utility, a formula has been obtained for the optimal Fourier spectrum noise by decomposing it into the superposition of power spectra of the dominant SSA eigenfilters. Finally, experiments have been performed with a computer-generated data set and a real-world smart-meter data set. Comparisons to other privacy approaches show that the optimized SSA-LDP does achieve a better data utility for a given data privacy.