基于分布式压缩感知和散列函数的数据融合隐私保护算法

针对群智感知网络数据融合传输过程中隐私泄露、信息不完整、数据窜改等安全问题，提出了一种基于分布式压缩感知和散列函数的数据融合隐私保护算法。首先，采用分布式压缩感知方法对感知数据进行稀疏观测，去除冗余数据；其次，利用单向散列函数求取感知数据观测值的散列值，将其和不受限的伪装数据一起填充到感知数据观测值中，达到隐藏真实感知数据的目的；最后，在汇聚节点提取伪装数据之后，再次获取感知数据的散列值并验证数据的完整性。仿真结果表明，该算法兼顾了数据的机密性和完整性保护，同时大大降低了通信开销，在实际应用中具有很强的适用性和可扩展性。

Data aggregation privacy protection algorithm based on distributed compressive sensing and hash function

Aiming at the security problems existing in the process of the data aggregation and transmission in crowd sensing networks, such as privacy leakage, incomplete information, data tampering, this paper proposed a data aggregation privacy protection algorithm based on distributed compressive sensing and hash function. Firstly, it used distributed compressive sensing method to sparsely observe the sensed data and remove the redundant data. Then, it utilized one-way hash function to obtain hash value of the observation data and filled the hash value with the unconstrained camouflage data into the observation data of sensory data to reach the aim of concealing the true sensor data. Finally, after extracting the camouflage data at the sink node, it obtained the hash value of the observation data again to verify the integrity of data. Simulation results show that the algorithm takes into account the privacy preserving and integrity protecting of data, and also can reduce the communication overhead greatly, which means the strong applicability and scalability in practical applications.