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

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    1804.01817v1.pdf
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    Genre
    Pre-print
    Date
    2019-11-25
    Author
    Cao, H
    Liu, S
    Wu, L
    Guan, Z
    Du, X
    Subject
    differential privacy
    fog computing
    internet of things
    non-intrusive load monitoring
    smart grid
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/4440
    
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    DOI
    10.1002/cpe.4528
    Abstract
    © 2018 John Wiley & Sons, Ltd. 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 draw the load curve and develop a plan or model 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 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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    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/4422
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