An Efficient and Anonymous Multidimensional Data Aggregation Scheme Based on Fog Computing for Smart Grids

With the development of smart grid and the diversification of their applications, privacy-preserving multi-dimensional data aggregation has been widely studied because it can analyze users’ electricity consumption more deeply. However, previous multidimensional data aggregation schemes suffer from heavy computational operations either to encrypt or decrypt the data. Additionally, existing fault-tolerant mechanisms require aggregation nodes to identify the IDs of smart meters, which leads to the risk of user privacy leakage. In this paper, we propose an efficient and anonymous multidimensional data aggregation scheme, called EAMA. In the proposed scheme, multidimensional data are encrypted by the Paillier encryption scheme of lower modular exponentiation, which reduces the computational cost. Moreover, to protect users’ privacy, smart meters upload data using pseudonyms in a fog computing-based architecture, which prevents aggregation nodes from accessing smart meters’ real IDs. Furthermore, a key challenge in the fault-tolerant phase is to eliminate blind factors, which EAMA effectively addresses without needing the real IDs of smart meters. Performance analysis demonstrates that EAMA achieves lower computational costs and satisfies security requirements.