Multifunctional and Multidimensional Secure Data Aggregation Scheme in WSNs
Cong Peng, Min Luo, Pandi Vijayakumar, Debiao He, Omar Said, Amr Tolba
Abstract
In wireless sensor networks (WSNs), data aggregation (DA) has become one of the most practical techniques to reduce processing delay and improve energy efficiency. To support intelligent applications, sensor nodes need to report heterogeneous and diverse data, which induce the demand for multidimensional DA and multifunctional data analysis. To solve the current security problems and functional requirements, we propose a multifunctional and multidimensional secure DA scheme to strike the balance between data availability and privacy. First, we design a Chinese remainder theorem conversion method with the counter to encode multidimensional data into large integers, which can be operated by linear homomorphic encryption schemes. Then, we introduce a multifunctional data analysis method supporting diversified aggregation functions, including linear, polynomial, and continuous functions. Moreover, we demonstrate that the proposed scheme can achieve confidentiality, integrity, authentication, and resistance against false data injection attacks. The experimental results show that the supported max dimension of one ciphertext in our scheme is at least twice that of existing schemes. Thus, in scenarios with high dimensions, our scheme is superior to the existing schemes in terms of computation and communication costs.