zkrpChain: Privacy-preserving Data Auditing for Consortium Blockchains Based on Zero-knowledge Range Proofs

Shiwei Xu, Xiaowen Cai, Yizhi Zhao, Zhengwei Ren, Lingjuan Wu, Huanguo Zhang, Le Du, Yan Tong

Abstract: Consortium blockchain has been widely used in different scenarios, where blockchain members demand that their uploaded data could be audited under their identities without exposing the data themselves. However, so far, no solution of privacy-preserving data auditing has been proposed. To address the problem, we propose zkrpChain, which focuses on protection of the integrity and privacy of the data uploaded by blockchain members while leaving their identities public. In zkrpChain, which is based on Hyperledger Fabric and Bulletproofs, both standard-range and arbitrary-range zero-knowledge range proofs generation and verification are supported. To improve the efficiency, the aggregation of multiple proofs and batch verification are also developed. For further development, we provide the client codes, chaincodes and related APIs. Finally, we conduct experiments to evaluate the performance of zkrpChain, and the results show that the consumed time (in an 8-thread scheme) of chaincodes and needed on-chain space of zkrpChain are very close to Bulletproofs, which is evaluated in a single-machine environment.