Privacy-preserving computation model for power trading based on homomorphic commitment and encryption

With the rapid development of distributed power trading, protecting transaction privacy while ensuring accurate settlements and regulatory compliance has become crucial. This paper proposes a privacy-preserving computation model for power trading based on homomorphic commitment and encryption. The model protects sensitive data, such as electricity prices, using homomorphic commitment and employs encryption to ensure the correctness and verifiability of transaction data. It supports accurate bill settlements, maintains transaction price privacy, and provides a traceable regulatory mechanism. Security proofs and simulations show that the model effectively prevents data tampering and malicious bidding, reduces market disorder risks, and offers an efficient, secure, and trustworthy solution for privacy protection and settlement in distributed power trading.