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Fog computing, a non-trivial extension of cloud computing to the edge of thenetwork, has great advantage in providing services with a lower latency. Insmart grid, the application of fog computing can greatly facilitate thecollection of consumer's fine-grained energy consumption data, which can thenbe used to draw the load curve and develop a plan or model for powergeneration. However, such data may also reveal customer's daily activities.Non-intrusive load monitoring (NILM) can monitor an electrical circuit thatpowers a number of appliances switching on and off independently. If anadversary analyzes the meter readings together with the data measured by anNILM device, the customer's privacy will be disclosed. In this paper, wepropose an effective privacy-preserving scheme for electric load monitoring,which can guarantee differential privacy of data disclosure in smart grid. Inthe proposed scheme, an energy consumption behavior model based on FactorialHidden Markov Model (FHMM) is established. In addition, noise is added to thebehavior parameter, which is different from the traditional methods thatusually add noise to the energy consumption data. The analysis shows that theproposed scheme can get a better trade-off between utility and privacy comparedwith other popular methods.

Achieving Differential Privacy against Non-Intrusive Load Monitoring in Smart Grid: a Fog Computing approach