Achieving differential privacy of data disclosure from non-intrusive load monitoring in smart grid

Abstract

In smart grid, large quantities of smart meters are installed in customers’ homes to collect electricity usage data, which can then be used to draw the load curve versus time of a day, and develop a plan or model for power generation. However, such data can also reveal customer’s daily activities. In addition, a non-intrusive load monitoring (NILM) device can monitor an electrical circuit that contains a number of appliances which switch on and off independently. If an adversary analyzes the meter readings together with the data measured by NILM device, the customer’s privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, Laplace noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.