Utility-Privacy Tradeoff Based on Random Data Obfuscation in Internet of Energy

Zhitao Guan, Guanlin Si, Jun Wu*, Liehuang Zhu, Zijian Zhang, Yinglong Ma


研究成果: Article査読

16 被引用数 (Scopus)


Internet of Energy is considered as a promising approach to solve the problems of energy crisis and carbon emission. It needs to collect user's real-time data for optimizing the energy utilization. However, such data may disclose user's privacy information. Previous works usually adopt specific obfuscation value to mask user's data and counteract the deviation through data aggregation; these works can preserve the data privacy effectively, but most of them consider less about the data-utility (precision). In this paper, we propose a utility-privacy tradeoff scheme based on random data obfuscation in Internet of Energy. In the proposed scheme, we adopt random data-obfuscation to mask the real-time data and realize the fault-tolerance during data aggregation, and the random obfuscation value obeys the Laplace distribution. We use the signal-to-noise ratio to quantify the level of utility; we measure the level of privacy through information entropy. Based on these two Indicators, we balance the utility-privacy tradeoff by calculating the optimal parameters of the Laplace distribution. The analysis shows that our scheme can meet the security requirement, and it also has better performance than that of other popular methods.

ジャーナルIEEE Access
出版ステータスPublished - 2017

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)


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