FGDA: Fine-grained data analysis in privacy-preserving smart grid communications

Abstract

In a smart grid environment, smart meters periodically collect and report information such as electricity consumption of users to a control center for timely monitoring, billing and other analytical purposes. There is, however, a need to ensure the privacy of user data, particularly when the data is combined with data from other sources. In this paper, we propose a new fine-grained data analysis (hereafter referred to as FGDA) scheme for privacy preserving smart grid communications. FGDA is designed to compute multifunctional data analysis (such as average, variance, and skewness) based on users’ ciphertexts, as well as supporting fault tolerance feature. We remark that FGDA can still function when some smart meters fail. Compared to existing schemes providing both the properties of multifunction and fault tolerance, FGDA is more efficient in terms of computation overheads. This is because FDGA does not use bilinear map or Pollard’s lambda method during decryption. We also demonstrate that FGDA achieves a higher communication efficiency, as the gateway only needs to send the ciphertext to the control center once even for different statistical functions.