Advancing User Privacy in Virtual Power Plants: A Novel Zero-Knowledge Proof-Based Distributed Attribute Encryption Approach

Abstract

In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants.