Deriving Customer Privacy from Randomly Perturbed Smart Metering Data

Abstract

Privacy has been one of the major concerns for customers in smart grids. Randomized perturbation based privacy-preserving smart metering methods, which are efficient and easy to implement, have recently become one of the commonly adopted solutions. However, it is a challenging task to meet utility companies' data collection requirements while protecting customer privacy. This paper analyzes the privacy protection capability of randomized perturbation based privacy-preserving smart metering methods. Both theoretical analysis and empirical studies show that statistical information of individual customers can still be accurately obtained from these randomly perturbed data. Also, an appliance usage inference method is proposed to accurately identify appliance operations of individual customers using randomly perturbed smart metering data. Evaluations using real-world smart metering data demonstrate that the proposed method can identify appliance operations with an accuracy between 92% and 99%.