The integration of privacy-preserving computation and blockchain have significantly advanced cross-institutional data sharing in financial regulation. However, challenges of transaction chain reconstruction and private information preservation remain unresolved, which causes illegal activities such as fraudulent trade due to the lack of inter-bank information interoperability. To address these issues, this paper proposes a collaborative supervision that combines Private Information Retrieval (PIR) and blockchain technology. Leveraging the PIR protocol, supervision authority can retrieve transactions associated with suspicious identity from various banks, without disclosing the retrieval target. Furthermore, blockchain is used to record and verify transactions, which helps to automatically correlate multi-account transaction chains and enables verifiable tracing of fund flows. Experimental results demonstrate that the transaction tracing scheme can achieve a single retrieval and batch retrieval with high execution efficiency. The running time only rises to 10.917 s (at \(1 \times 10^7\) data size) for batch retrieval of 1000 data entries, which is within an acceptable range for financial regulatory scenarios

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Privacy-Preserving Transaction Chain Retrieval and Reconstruction for Collaborative Supervision

  • Yuhan Yang,
  • Qian Xu,
  • Huajie Shen,
  • Bo Yu,
  • Wei He,
  • Lijun Wei,
  • Jing Wu,
  • Chengnian Long

摘要

The integration of privacy-preserving computation and blockchain have significantly advanced cross-institutional data sharing in financial regulation. However, challenges of transaction chain reconstruction and private information preservation remain unresolved, which causes illegal activities such as fraudulent trade due to the lack of inter-bank information interoperability. To address these issues, this paper proposes a collaborative supervision that combines Private Information Retrieval (PIR) and blockchain technology. Leveraging the PIR protocol, supervision authority can retrieve transactions associated with suspicious identity from various banks, without disclosing the retrieval target. Furthermore, blockchain is used to record and verify transactions, which helps to automatically correlate multi-account transaction chains and enables verifiable tracing of fund flows. Experimental results demonstrate that the transaction tracing scheme can achieve a single retrieval and batch retrieval with high execution efficiency. The running time only rises to 10.917 s (at \(1 \times 10^7\) data size) for batch retrieval of 1000 data entries, which is within an acceptable range for financial regulatory scenarios