Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
Junhong Liu, Qinfei Long, Rong-Peng Liu, Wenjie Liu, Xin Cui, Yunhe Hou
TL;DR
This work addresses privacy leakage in fully distributed P2P energy trading by introducing a hybrid privacy-preserving framework that combines CRT-Paillier encryption for secure two-party and multi-party computations with a tailored secret-sharing scheme for distributed optimization. It derives a feasible range for random encryption coefficients to guarantee linear convergence of the PDHG-based distributed optimization while maintaining privacy without a trusted third party ($r_i$ and $oldsymbol{ abla}$ updates are controlled to ensure $l_i<1$). The authors provide rigorous convergence and privacy analyses (Theorem 1, Theorem 2, Corollary 1) and validate via numerical simulations on IEEE distribution networks, showing exactness of the privacy-preserving approach and competitive computation times against standard Paillier. This work enables scalable, private, and exact P2P energy trading in realistic networks, facilitating secure local energy balancing without centralized trust.
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.