An Efficient and Fine-Grained Access Control Scheme for Multidimensional Data Aggregation in Smart Grid

Abstract

Smart grid has a large number of users and numerous data analysis organizations. The privacy-preserving and access control for multidimensional power data have become vital security concerns. For correct aggregation of multidimensional data, existing schemes require transmitting attribute sets of data together with ciphertexts to the gateway (GW), which leaks the relevant information of power data and increases the security risk. Moreover, to implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions. It means CC can access all of the data without authorization, which may result in the data leakage. For coping with these problems, we propose an efficient and Fine-grained Access control scheme for Multidimensional data Aggregation, named FAMA. It uses the extended Boneh-gohnissim homomorphic encryption (EBGN) to enforce multidimensional privacy-preserving data aggregation without transmitting attribute sets and attribute-based encryption (ABE) to implement fine-grained access control at dimension-level. By using FAMA, GW does not need to distinguish different ciphertexts. And every receiver can only extract the specific dimensions that it has been authorized, which not only restricts the privileges of CC, but also simplifies the operations of GW and CC. Moreover, after optimizing the scalar multiplication on elliptic curve, the encryption of FAMA is more efficient than the existing schemes.