Research Area:  Fog Computing
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 consumers fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customers 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 customers 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, 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.
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Author(s) Name:  Hui Cao,Shubo Liu,Longfei Wu,Zhitao Guan,Xiaojiang Du
Journal name:  Concurrency and Computation: Practice and Experience
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Publisher name:  Wiley
DOI:  10.1002/cpe.4528
Volume Information:  Volume 31, Issue 22
Paper Link:   https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4528