The Research On Precise Data Aggregation Privacy-preserving Algorithm In Wireless Sensor Networks

With the rapid development of information technology nowadays, one has became a hot-spot of studies that been paid a lot of attentions internationally is WSN(Wireless Sensor Network).As an significant component of IoT(Internet of Thing),WSN’s main method for sensitive information protection is inserting the privacy protection scheme during the process of data aggregation. However, it seems that there are no suitable privacy protection schemes for WSN have been existed in other fields, which with the reason that WSN has the characteristics of node limitation and structure self-organization. Thus, the researches on data privacy protection of WSN do make sense.In this thesis, we focus on two main performances of data aggregation privacy protection scheme, the precision and the communication traffic. Further, analysis and comparison are made between the proposed approach and the representative one. Some defects of the classical CPDA (Cluster-based Private Data Aggregation) algorithm is discussed emphatically. Based on the idea of clustering and the technique of data disturbance CPDA protects the privacy of data resultfully, however, with negative traffic and aggregation precision. The proposed algorithm, OCPDA (Optimized-CPDA), raises an optimized scheme for data aggregation privacy protection. On one hand, by introducing of’partial data exchange’strategy, which reduces unnecessary data exchange, that cut traffic down to a certain degree. On the other hand, because of the implementation of’randomly send’strategy the probability of data collision reduced and the precision of data aggregation improved.Both the derivation and simulation results shows the fact that OCPDA, with two optimized strategies, is able to achieve the desired results. Indeed, in the case of traffic, OCPDA has less total number of data packets than CPDA. When the size of cluster equal to 3,OCPDA has 28% packets less than CPDA, and when the size of cluster equal to 6,40% shorter, respectively. In the case of aggregation precision, OCPDA still outperform CPDA.