POMF: A Privacy-preserved On-chain Matching Framework

With the growth of the Internet, an increasing number of online matching platforms have emerged. However, current online matching platforms need to address three open challenges to improve user experience: (1) trans-parency and inherent issues with centralized platforms; (2) privacy concerns of users' sensitive information; and (3) high service fees for matching services. In this paper, we propose a Privacy-preserved On-chain Matching Framework (POMF) that is suitable for a broad range of matching scenarios to overcome the challenges above simultaneously. In order to address the first challenge, POMF is constructed via the decentralized blockchain, based on which smart contracts are utilized to execute matching services automatically and publicly. To address the second challenge, we combine Order-Preserved Encryption (OPE) with Software Guard Extensions (SGX) to design a privacy-preserved on-chain matching mechanism that can enable information matching among different users in the encrypted domain, while revealing only order information and maintaining confidentiality. To address the third challenge, we optimize the OPE algorithm to reduce its computing and communication overhead, which further reduces the matching service fee. Besides, we ensure the fairness of POMF by designing a reasonable price mechanism that prices the contact information based on the matching degree between the involved matched users. Finally, we prove the availability of POMF via an comprehensive analysis and demonstrate the practicality of POMF by implementing a prototype and deploying it on a Fabric test network with extensive simulations.