A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance

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• 作者：Haiyong Bao ; Rongxing Lu
• 关键词：Smart grid ; Data aggregation ; Privacy preservation ; Differential privacy ; Fault tolerance
• 刊名：Peer-to-Peer Networking and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：10
• 期：1
• 页码：106-121
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Communications Engineering, Networks; Information Systems and Communication Service; Computer Communication Networks; Signal,Image and Speech Processing;
• 出版者：Springer US
• ISSN：1936-6450
• 卷排序：10

文摘

To design an efficient and secure data aggregation scheme fitting real applications has been pursued by research communities for a long time. In this paper, we propose a novel secure data aggregation scheme to simultaneously achieve privacy preservation and data integrity with differential privacy and fault tolerance. Specifically, by introducing some auxiliary ciphertext subtly, a novel distributed solution for fault tolerant data aggregation is put forward to be able to aggregate the functioning smart meter measurements flexibly and efficiently for any rational number of malfunctioning smart meters with discretional long failure period. The proposed scheme also achieves a good tradeoff of accuracy and security of differential privacy for arbitrary number of malfunctioning smart meters. In the proposed scheme, a novel efficient authentication mechanism is also proposed to generate and share session keys in a noninteractive way, which is leveraged for AES encryption to achieve source authentication and data integrity of the transmitted data. Furthermore, through decentralizing the computational overhead and the authority of the hub-like entity of the gateway, the security of our proposed scheme is enhanced and the efficiency is improved significantly. Finally, extensive performance evaluations are conducted to illustrate that the proposed data aggregation scheme outperforms the state-of-the-art similar schemes in terms of computation complexity, communication cost, robustness of fault tolerance, and utility of differential privacy.