TY - JOUR
T1 - Achieving differential privacy against non-intrusive load monitoring in smart grid
T2 - A fog computing approach
AU - Cao, Hui
AU - Liu, Shubo
AU - Wu, Longfei
AU - Guan, Zhitao
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2018 John Wiley & Sons, Ltd.
PY - 2019/11/25
Y1 - 2019/11/25
N2 - 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.
AB - 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.
KW - differential privacy
KW - fog computing
KW - internet of things
KW - non-intrusive load monitoring
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85073679943&partnerID=8YFLogxK
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U2 - 10.1002/cpe.4528
DO - 10.1002/cpe.4528
M3 - Article
AN - SCOPUS:85073679943
SN - 1532-0626
VL - 31
JO - Concurrency and Computation: Practice and Experience
JF - Concurrency and Computation: Practice and Experience
IS - 22
M1 - e4528
ER -