Secure and efficient multifunctional data aggregation without trusted authority in edge-enhanced IoT

Since the rapid development of Internet of Things (IoT) has promoted the dramatic growth of data, data aggregation has received considerable attention, which can collect the sensed data from IoT devices and analyze them for high-quality information to greatly benefit our life. Nevertheless, these sensed data may be related to individual sensitive information and are vulnerable to security threats. To solve the problem, many secure data aggregation schemes have been proposed. However, most of them either have a high overhead, only support sum aggregation, or need the involvement of a trusted third party. They cannot strike a good balance between security, efficiency and functionality. In this paper, we propose a Secure and Efficient Multifunctional Data Aggregation without Trusted Authority (TA) in Edge-Enhanced IoT (SEMDA). In our method, combined with lightweight cryptographic techniques, a robust, multifunctional and fine-grained data aggregation scheme is constructed. In addition, considering the potential privacy disclosure caused by deep association between IoT data, we extend SEMDA to support differential privacy. A comprehensive security analysis of SEMDA is conducted under the honest-but-curious model, and the results show that our scheme not only achieves confidentiality and privacy, but also guarantees integrity and authentication. Meanwhile, extensive theoretical analyses and experimental evaluations demonstrate that SEMDA performs more efficiently with respect to computation and communication while retaining more desired properties.