Mechanism and Communication Co-Design for Differentially Private Energy Sharing

2026-04-05

AI Summary

This work addresses the challenge of preserving prosumer privacy in energy-sharing systems under communication constraints, where existing mechanisms fall short. It proposes a novel energy-sharing framework that jointly designs differential privacy with over-the-air MIMO signal aggregation for the first time, achieving a balance between privacy protection and system efficiency. By integrating differentially private perturbations directly into the wireless MIMO aggregation process and coupling this with an equilibrium-seeking algorithm, the method effectively prevents the platform from reconstructing private parameters through base station observations while enabling near-optimal energy allocation. Both theoretical analysis and empirical experiments confirm the convergence of the proposed algorithm and the robustness of its privacy guarantees.

Abstract

Integrating distributed energy resources (DERs) is a critical step toward addressing the global climate crisis. This transformation has driven the transition from traditional consumers to prosumers and given rise to new energy sharing business models. Existing works have extensively studied prosumer energy sharing mechanisms, yet little attention has been paid to privacy protection, particularly when communication constraints are taken into account. In this paper, we study an energy sharing mechanism where information is exchanged over wireless channels via over-the-air (OTA) multiple-input multiple-output (MIMO) aggregation to exploit spectral efficiency for scalable prosumer coordination. To characterize the privacy leakage risk during data transmission process, we introduce an adversarial attack model and demonstrate that, under certain conditions, the platform can extract and recover prosumers' private parameters from the base station observations. To safeguard the energy sharing mechanism against such attacks, we propose a differentially private equilibrium-seeking algorithm, analyze the achievable privacy level, and establish convergence guarantees that quantify the impact of privacy on the convergence accuracy. Numerical experiments demonstrate that our approach effectively protects prosumers' privacy while converging to near-optimal solutions.

Problem

• differential privacy
• energy sharing
• privacy protection
• wireless communication
• prosumers

Innovation

• differential privacy
• over-the-air aggregation
• MIMO
• energy sharing
• privacy-preserving algorithm