Privacy-preserving data aggregation in smart power grid systems

Abstract

Smart Meters (SMs) are IoT end devices used to collect user utility consumption withlimited processing power on the edge of the smart grid (SG). While SMs have significantapplications in providing data analysis to the utility provider and consumers, private userinformation can be inferred from SMs readings. Several methods are developed in theliterature that uses perturbation by adding noise to alter user load, hide consumer data, and preserve privacy. Most practices limit the amount of perturbation noise using differential privacy to protect the benefits of data analysis. However, additive noise perturbation may have an undesirable effect on billing. We present a virtual battery model that uses perturbation with additive noise obtained from a virtual chargeable battery. Our model uses fog aggregation with authentication and encryption that employs lightweight cryptographic primitives. We use Diffie-Hellman with a two-way challenge response method for symmetrical key exchange. A hash-based message authentication code (HMAC) is used for integrity and authenticity, and Advanced Encryption Standard (AES) for encryption. We present our differentially private model with bounding parameters and a dynamic window algorithm to preserve privacy budget loss in infinite time series