An Efficient Privacy and Integrity Preserving Data Aggregation Scheme for Multiple Applications in Wireless Sensor Networks

Abstract

For wireless sensor networks (WSNs), data aggregation is an important method to reduce the energy consumption. Privacy-integrity preserving data aggregation is one of typical WSNs applications. Many secure aggregation schemes based on homomorphic encryption have been proposed in the past years. However, most of them cannot aggregate hybrid sensors' data into one in some real WSN applications, accordingly, they don't satisfy multi-application environments and heterogeneous sensors. To address this challenge, we present an efficient Privacy and Integrity preserving data aggregation scheme for Multiple Applications in WSNs (abbreviated as PIMA). The proposed PIMA employs the homomorphic Paillier encryption and the homomorphic MAC to not only protect data privacy but also check the aggregation data integrity. For multi-application environments, PIMA can efficiently aggregate hybrid sensors' data into one, and extract application-specific data from aggregated ciphertexts by Chinese Remainder Theorem (CRT) at the base station (BS). In additional, the proposed PIMA can early filter injected false data in aggregator, and achieve fault-tolerant in case that some sensors is malfunctioning or data losing. Detailed security analysis and performance evaluations show PIMA is privacy-enhanced, secure, robust and efficient. To the best of our knowledge, this is the first work that provides both integrity and privacy for multi-application in WSNs.