Privacy-Preserving Energy Sharing Over Converged Communication-Energy-Compute Networks

Abstract

The rapid growth of edge cloud infrastructure introduces new challenges in managing energy consumption data while ensuring privacy during energy trading. Existing studies focused on smart metering privacy, local electricity markets, and privacy-aware control, whereas privacy-preserving cloudlet energy trading with battery support remains largely unexplored. This paper addresses these challenges by proposing an approach that effectively motivates cloudlets to trade their superfluous energy to minimize energy cost while protecting the privacy of energy usage information and the corresponding underlying data processing patterns. By applying Lyapunov optimization in coupling with differential privacy (DP), we develop a privacy-preserving energy trading algorithm where the cloudlet battery level and data backlog are modeled as virtual queues. This transforms the problem of interest from a long-term average objective to a convex per-instant optimization problem solved effectively. We analytically establish the trade-off between minimizing energy costs and maintaining the stability of both data queues and batteries. It is revealed that the effect of DP on average system costs can be minimized by allowing higher data queue and battery levels, ensuring robust privacy protection without sacrificing cost efficiency. The minimum capacity requirement for the batteries is identified for privacy guarantee. Simulations confirm the effectiveness of our approach, demonstrating up to a 34% reduction in average energy costs for cloudlets.