A secure and efficient privacy-preserving data aggregation algorithm
Hui Dou, Yuling Chen, Yixian Yang, Yangyang Long
Abstract
Abstract As a significant part of the Internet of things, wireless sensor networks (WSNs) is frequently implemented in our daily life. Data aggregation in WSNs can realize limited transmission and save energy. In the process of data aggregation, node data information is vulnerable to be eavesdropped and attacked. Therefore, it is of great significance to the research of data aggregation privacy protection in WSNs. We propose a secure and efficient privacy-preserving data aggregation algorithm (SECPDA) based on the original clustering privacy data aggregation algorithm. In this algorithm, we utilize SEP protocol to dynamically select cluster head nodes, introduce slicing idea for the private data slicing, and generate false information for interference. A comprehensive experimental evaluation is conducted to assess the data traffic and privacy protection performance. The results demonstrate that the proposed SECPDA algorithm can effectively reduce data traffic and further improve data privacy of nodes.