Privacy-preserving data analytics for smart decision-making energy systems in sustainable smart community

Yuping Zhang, Youyang Qu, Longxiang Gao, Tom Hao Luan, Alireza Jolfaei, James Xi Zheng

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Nowadays, smart community becomes as a key component of Internet of Things (IoT). The fast booming of it significantly enhances the application of smart cities. To achieve data analytics and decision making for energy systems in smart cities, we propose use fog computing to establish a smart community due to the reduction of bandwidth consumption and latency. However, privacy issues are emerging in this scheme because continuous attacks put sensitive information under great threats. Besides, the loss of sensitive information degrade the quality of data analytics and decision making. Motivated by this, we propose a differentially private smart community model with game theory-based personalized privacy protection (GPDP). Differential privacy is deployed in a personalized way while a logarithmic function is leveraged to map data sensitivity to privacy protection level. Then we use game theory to model the confrontation and further derive optimized trade-off, which is denoted by the Nash Equilibrium. In addition, we develop a modified reinforcement learning algorithm to achieve fast convergence. The advanced evaluation results show the advantages of the proposed model from the perspective of the optimized trade-off between personalized privacy protection and data utility, which improves data analytics and decision-making performances for energy management in smart cities.

Original languageEnglish
Article number103144
Number of pages9
JournalSustainable Energy Technologies and Assessments
Early online date10 Apr 2023
Publication statusPublished - Jun 2023
Externally publishedYes


  • Data analytics
  • Energy management
  • Fog computing
  • Game theory
  • Personalized privacy protection
  • Smart community


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