Achieving differential privacy against non-intrusive load monitoring in smart grid: A fog computing approach

摘要

Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine-grained energy consumption data, which can then be used to drawthe load curve and develop a plan ormodel for power generation. However, such data may also reveal customer's daily activities. Non-intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric loadmonitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behaviormodel based on Factorial HiddenMarkovModel(FHMM)is established. In addition, 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 showsthat the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.