A Robust Privacy-Preserving Data Aggregation Scheme for Edge-Supported IIoT
Shuai Shang, Xiong Li, Ke Gu, Lei Li, Xiaosong Zhang, Pandi Vijayakumar
Abstract
Edge-supported Industrial Internet of Things (IIoT) has received remarkable attention recently since edge computing can not only reduce bandwidth consumption but also decrease the response time of industrial systems. However, the sensed data in the industrial environment is considered private. Thus, the data cannot be directly aggregated at the server due to privacy leakage. Although several privacy-preserving-aggregated schemes have been proposed, their security goals are not strong enough. Besides, most schemes are inefficient for resource-constrained devices. Aiming at solving the abovementioned problems, this article proposes a robust privacy-preserving data aggregation scheme for edge-supported IIoT. Specifically, the scheme adopts the Paillier cryptosystem to protect the privacy of users. Additionally, it utilizes ECDSA signature to support batch verification of multiple signatures from different signers, which significantly improves efficiency. Security analysis shows that the proposed scheme not only guarantees the integrity of the data and mutual authentication among entities but also realizes differential privacy protection. Extensive experiments are conducted to compare our scheme with the related work. The results show that our method outperforms most of the compared schemes with respect to communication. Moreover, compared with the related work, our scheme reduces the computational cost by an average of 6.7%, 16.9%, and 27.8% in sensor, edge server, and control center sides, respectively.