Authors: Cong Peng, Min Luo, Pandi Vijayakumar, Debiao He, Omar Said, Amr Tolba

Title: Multi-Functional and Multi-Dimensional Secure Data Aggregation Schemes in WSNs

Journal: IEEE Internet of Things Journal

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 induces the demand for multi-dimensional data aggregation and multi-functional data analysis. To solve the current security problems and functional requirements, we propose a multi-functional and multi-dimensional secure data aggregation scheme to strike the balance between data availability and privacy. Firstly, we design a Chinese remainder theorem conversion method with the counter to encode multi-dimensional data into large integers, which can be operated by linear homomorphic encryption schemes. Then, we introduce a multi-functional 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. 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.