MDA-FQ: A Multidimensional Privacy-Preserving Data Aggregation Scheme with Function Query for Smart Grid

Smart grids enable advanced functions like power dispatching, real-time pricing, and electricity theft detection. However, these capabilities have also raised significant concerns about user privacy. Privacy-preserving data aggregation technology enables the collection and utilization of users’ power consumption data without compromising their privacy. However, existing multidimensional data aggregation approaches fail to ensure user privacy and provide query services for data consumers in different roles. To address these problems, a multidimensional data aggregation scheme with function query (MDA-FQ) is proposed to realize the value of the multidimensional consumption data while safeguarding user privacy. First, MDA-FQ employs inner-product functional encryption and the Chinese Remainder Theorem to securely encrypt users’ multidimensional power consumption data. Second, a role-based data access mechanism is introduced to enable data consumers with varying permissions to request different types of data from cloud service providers, thus maximizing the utility of multidimensional data under privacy constraints. Third, MDA-FQ incorporates differential privacy on multidimensional data to ensure that each data dimension is protected against differential attacks, thereby enhancing overall privacy protection. Security analyses indicate that MDA-FQ can effectively counter differential attacks and other privacy risks. Extensive experimental evaluations further demonstrate that MDA-FQ not only supports versatile queries on multidimensional data but also significantly reduces computation and communication overhead, making it both practical and scalable.