PFDAM: Privacy-Preserving Fine-Grained Data Aggregation Scheme Supporting Multifunctionality in Smart Grid

In smart grids, fine-grained data aggregation with multifunctionality is essential for utilities to accurately predict power supply-and-demand balance and perform secure data processing. Constructing such a powerful privacy-preserving data aggregation scheme, requires considerations for fine-grained data aggregation, data privacy, integrity, unlinkability, and fault tolerance of the protocol. However, existing data aggregation schemes fail to provide all these functionalities because the introduction of certain technologies in these schemes make some functions incompatible with each other, such as digital signatures, guarantee data integrity but lead to data linkability. Therefore, it is a challenge to construct a data aggregation scheme that satisfies the above functionalities. To address it, by combining hash-based message authentication code (HMAC) and public key encryption with equality testing, we propose a fine-grained privacy-preserving data aggregation scheme supporting multifunctionality (PFDAM). The proposed scheme can provide the above functionalities and resist malicious data mining attacks launched by internal or external attackers. Detailed security proofs and performance analysis show that our PFDAM system satisfies the smart grid system's desired security properties and efficiency. Finally, compared with several recent schemes, experimental results indicate that our PFDAM is practically applicable in terms of performance.