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异构 WSN 中基于参考值的可恢复隐私保护数据融合

徐浚诚%游林   

  • 基金资助:
    国家自然科学基金[61272045]、浙江省自然科学基金(R1090138)

Reference-based Recoverable Concealed Data Aggregation in Heterogeneous Wireless Sensor Networks

XU Jun-cheng%YOU Lin   

  • About author:杭州电子科技大学密码与信息安全研究所,浙江杭州,310018

摘要: 文章介绍了无线传感网络的安全数据融合技术,针对当前数据融合算法安全性不高、开销过大以及融合精度低等问题,提出了基于参考值的可恢复隐私保护数据融合算法。该算法利用公钥同态加密机制为数据提供端到端的机密性和完整性认证。此外,该算法动态地为网络中的每个节点设定下次传输的参考值,并通过传输采样值和参考值之间的差值来减少网络中的数据传输量。仿真结果表明,该算法可以有效地减少数据传输量,提高整个网络的能量和带宽效率。

Abstract: This paper introduces secure data aggregation in wireless sensor networks. In order to solve the problems of security, large overhead and low aggregation precision in current data aggregation schemes, this paper proposes a reference-based recoverable concealed data aggregation algorithm(R-RCDA). The algorithm provides end-to-end confidentiality and integrity authentication for the transmitted data by employing the public-key based privacy homomorphic encryption scheme. Besides, the algorithm sets a new reference for every sensor node dynamically, and the reference will be used in the next round of transmission. It can reduce the amount of transmission by transmitting the difference between the reference and the row sensed data. The experimental result shows that R-RCDA can reduce the amount of data transmission effectively and then improve the energy and bandwidth efficiency of the whole network.