Privacy-preserving ADMM-based multi-agent energy sharing framework between communities using Ring Signature Multi-Party Computation

A critical challenge in community energy management is effectively integrating numerous consumers and prosumers into scalable energy-sharing systems. This paper proposes an innovative privacy-preserving distributed optimization framework leveraging the Alternating Direction Method of Multipliers (ADMM) and Ring Signature Multi-Party Computation (RSMPC). Centralized and distributed models are compared in terms of scalability. Privacy concerns within decentralized optimization are addressed by introducing a ring signature-based cryptographic protocol. Unlike traditional methods such as differential privacy or multi-key homomorphic encryption, our proposed RSMPC scheme balances computational efficiency and rigorous privacy guarantees. The multi-agent architecture, implemented using the JADE platform, demonstrates significant improvements in scalability and privacy preservation.