Privacy-preserving data aggregation against malicious data mining attack for IoT-enabled smart grid

Jing Wang, Libing Wu, Sherali Zeadally, Muhammad Khurram Khan, Debiao He

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may consume a lot of bandwidth, and even put the user's privacy at risk. Although many privacy-preserving data aggregation schemes have been proposed to solve the problem, they still suffer from some security weaknesses or performance deficiency, such as lack of satisfactory data confidentiality and resistance to malicious data mining attack. To address these issues, we propose a novel privacy-preserving data aggregation scheme (called PDAM) for IoT-enabled smart grids, which can support efficient data source authentication and integrity checking, secure dynamic user join and exit. Unlike existing schemes, the PDAM is resilient to the malicious data mining attack launched by internal or external attackers and can achieve perfect data confidentiality against not only a malicious aggregator but also a curious control center for an authorized user. The detailed security and performance analysis show that our proposed PDAM can satisfy several well-known security properties and desirable efficiency for a smart grid system. Moreover, the comparative studies and experiments demonstrate that the PDAM is superior to other recently proposed works in terms of both security and performance.

Original languageEnglish
Article number3440249
JournalACM Transactions on Sensor Networks
Issue number3
StatePublished - Aug 2021

Bibliographical note

Funding Information:
The work was supported by the Major Scientific and Technological Innovation Project of Shandong Province (No. 2020CXGC010115), the National Natural Science Foundation of China (Nos. 61772377, 61932016, 61972294, U20A20177), the Special Project on Science and Technology Program of Hubei Province (No. 2020AEA013), the Natural Science Foundation of Hubei Province (No. 2020CFA052), the Wuhan Municipal Science and Technology Project (No. 2020010601012187), and the Science and Technology Planning Project of ShenZhen (JCYJ20170818112550194). Muhammad Khurram Khan is supported by Researchers Supporting Project number (RSP-2020/12), King Saud University, Riyadh, Saudi Arabia. Authors’ addresses: J. Wang, School of Computer Science, Wuhan University, Wuhan, China, 430072; email:; L. Wu, School of Computer Science, Wuhan University, Wuhan, China, 430072, School of Cyber Science and Engineering, Wuhan University, Wuhan, China, 430072, Shenzhen Research Institute of Wuhan University, Shenzhen, China, 518057; email:; S. Zeadally, College of Communication and Information, University of Kentucky, Lexington; email:; M. K. Khan, Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Saudi Arabia, 11451; email:; D. He, School of Cyber Science and Engineering, Wuhan University, Wuhan, China, 430072; email: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from © 2021 Association for Computing Machinery. 1550-4859/2021/06-ART25 $15.00

Publisher Copyright:
© 2021 Association for Computing Machinery.


  • Data aggregation
  • Malicious data mining attack
  • Privacy-preserving
  • Smart grid

ASJC Scopus subject areas

  • Computer Networks and Communications


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