Traditional financial institutions face inefficiencies that can be addressed by distributed ledger technology. However, a primary barrier to adoption is the privacy concerns surrounding publicly available transaction data. Existing private protocols for distributed ledger that focus on the Ring-CT model are not suitable for adoption for financial institutions. We propose a post-quantum, lattice-based transaction scheme for encrypted ledgers which better aligns with institutions' requirements for confidentiality and audit-ability. The construction leverages various zero-knowledge proof techniques, and introduces a new method for equating two commitment messages, without the capability to open one of the commitment during the re-commitment. Subsequently, we build a publicly verifiable transaction scheme that is efficient for single or multi-assets, by introducing a new compact range-proof. We then provide a security analysis of it. The techniques used and the proofs constructed could be of independent interest.
Towards Federated Graph Learning for Collaborative Financial Crimes Detection
IBM T.J. Watson Research Center（IBM T.J. 沃森研究中心）；IBM Research Almaden（IBM阿尔马登研究院）；IBM Research Tokyo（IBM东京研究院）；IBM Global Business Services（IBM全球企业咨询服务部）
Scalable and Verifiable Federated Learning for Cross-Institution Financial Fraud Detection
School of Computer Engineering, KIIT Deemed to be University（计算机工程学院，KIIT deemed to be 大学）；School of Electronics Engineering, KIIT Deemed to be University（电子工程学院，KIIT deemed to be 大学）
FinBTech: Blockchain-Based Video and Voice Authentication System for Enhanced Security in Financial Transactions Utilizing FaceNet512 and Gaussian Mixture Models
Privacy-Preserving Credit Risk Prediction with Alternative Data
School of Management and Economics, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳）管理学院）；School of Management, Fudan University（复旦大学管理学院）；Lerner College of Business and Economics, University of Delaware（特拉华大学勒纳商学院）
