In smart grids, real-time electricity data uploaded by smart meters may be analyzed by an attacker with other data analytics methods, which may expose users’ privacy. To ensure user privacy, differential privacy methods are often used to process data. However, these methods reduce the accuracy of the data results obtained by the center and lead to unavailability of the data. In this paper, we address this problem and propose a distributed differential privacy protection scheme. Two methods of data noise addition and data perturbation are fused and used in the protection scheme. Data accuracy is improved by optimizing the noise generation method. To address the problem of quantitatively balancing the users’ privacy needs with the central analytics needs, this paper describes the needs of both through mathematical definitions, i.e., data accuracy and data privacy, and proposes a privacy budget that balances data accuracy and privacy. The performance of the proposed scheme is evaluated using the typical power data, which proves the excellent performance.
Li et al. (Sun,) studied this question.