By integrating the traditional power grid with information and communication technology,smart grid achieves dependable,efficient,and flexible grid data processing.The smart meters deployed on the user side of the smart grid collect the users'power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition.The control center can evaluate the supply and demand of the power grid through aggre-gated data from users and then dynamically adjust the power supply and price,etc.However,since the grid data collected from users may disclose the user's electricity usage habits and daily activities,privacy concern has become a critical issue in smart grid data aggregation.Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.In this paper,we propose a privacy-preserving smart grid data aggregation scheme satisfying Local Differential Privacy(LDP)based on randomized responses.Our scheme can achieve an efficient and practical estimation of power supply and demand statistics while preserving any individual participant's privacy.Utility analysis shows that our scheme can estimate the supply and demand of the smart grid.Our approach is also efficient in terms of computing and communication overhead,according to the results of the performance investigation.

Na Gai;Kaiping Xue;Bin Zhu;Jiayu Yang;Jianqing Liu;Debiao He

School of Cyber Science and Technology,University of Science and Technology of China,Hefei,Anhui,230027,China;Department of Electronic Engineering and Information Science,University of Science and Technology of China,Hefei,Anhui,230027,China;Department of Electrical and Computer Engineering, University of Alabama in Huntsville,Huntsville,AL,35899,USA;Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan,Beihu,430072,China

数字通信与网络（英文）

[1]Na Gai;Kaiping Xue;Bin Zhu;Jiayu Yang;Jianqing Liu;Debiao He-.An efficient data aggregation scheme with local differential privacy in smart grid)[J].数字通信与网络（英文）,2022(03):333-342

dependable

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