传感器网络数据处理中基于隐私向量的隐私保护机制

Data processing based on the privacy-preserving vector for wireless sensor networks

针对传感器网络数据处理中的隐私保护需求,提出了新的分布式机制。构造了隐私向量,并设计了低能耗的隐私向量生成方法及使用方法,从而可有效实现求和、求最值及压缩等各类处理中的数据隐私保护。提出了种子分发算法,保证了隐私向量的安全动态生成。理论分析和仿真实验表明,与已有同类机制相比,新机制不仅能更好地抵御节点俘获攻击,具有更高的隐私保护有效性,且更为能量有效。

To solve the privacy disclosing problem during the data processing phase of wireless sensor networks, a distributed mechanism was proposed. Privacy-preserving vector（vector for short） was constructed. Moreover, lightweight method for vector generation and novel method for using the vector were also presented. Thus, the methods were able to solve the privacy-preserving problem for various data processing funtions such as max/min and data compression efficiently. An algorithm based on data hiding and slice was given and then the vector was able to be generated securely and dynamically. Extensive analyses and experiments show that the mechanism is more robust to node compromise attack and thus can preserve privacy more efficiently. Moreover, the mechanism consumes less power.