Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations

TL;DR

This paper introduces a hybrid secure computation framework to enable privacy-preserving peer-to-peer energy trading, effectively protecting prosumer data while ensuring accurate and scalable energy market operations.

Contribution

It develops a novel hybrid secure computation method combining secret sharing and Paillier encryption for privacy in distributed energy trading.

Findings

The approach guarantees data security during trading.

Numerical simulations confirm the method's accuracy and scalability.

The scheme effectively prevents privacy leakage in P2P energy markets.

Abstract

The massive integration of uncertain distributed renewable energy resources into power systems raises power imbalance concerns. Peer-to-peer (P2P) energy trading provides a promising way to balance the prosumers' volatile energy power generation and demands locally. Particularly, to protect the privacy of prosumers, distributed P2P energy trading is broadly advocated. However, severe privacy leakage issues can emerge in the realistic fully distributed P2P energy trading paradigm. Meanwhile, in this paradigm, two-party and multi-party computations coexist, challenging the naive privacy-preserving techniques. To tackle privacy leakage issues arising from the fully distributed P2P energy trading, this paper proposes a privacy-preserving approach via hybrid secure computations. A secure multi-party computation mechanism consisting of offline and online phases is developed to ensure the security of shared data by leveraging the tailored secret sharing method. In addition, the Paillier encryption method based on the Chinese Remainder Theorem is proposed for both the secure two-party computation and the offline phase of the multi-party computation. The random encryption coefficient is designed to enhance the security of the two-party computation and simultaneously guarantee the convergence of the distributed optimization. The feasible range for the encryption coefficient is derived with a strict mathematical proof. Numerical simulations demonstrate the exactness, effectiveness, and scalability of the proposed privacy-preserving approach.