A Privacy-Preserving Energy Consumption Data Sharing Framework for Smart Grids

In the era of big data, smart grid utilities are seeking new opportunities to create profits by making use of in-hand power big data. However, these data, such as energy consumption data, power grid measurement data, etc., are often sensitive digital assets that cannot be directly shared to third parties or individual users. To meet this gap, in this paper we propose a Privacy-Preserving energy consumption Data Sharing framework (PPDS) for smart grids. In this framework, two Paillier-based algorithms are designed respectively to achieve privacy-preserving data acquisition and secure computation of the mean for energy consumption data in a specific period or regions. Numerical results demonstrate that the proposed PPDS framework is efficient in privacy-preserving energy consumption data sharing.