Year: 2024
Type: conference-paper
Abstract: The continuously increasing volume of blockchain data presents significant challenges to blockchain traceability. Current tracing approaches, which rely on heuristic analytics directly applied to blockchain data, exhibit degraded tracking accuracy due to the involvement of only partial data. On the other hand, incorporating all blockchain data for analytics is not scalable given the unprecedented data volumes. Interestingly, we observe that despite the vast amount of blockchain data, blockchain tracing primarily focuses on time-related transaction spaces rather than all transactions. This insight motivates us to rethink the blockchain tracing problem to achieve both accuracy and scalability.
Authors: Xiangao Chen, Tianyu Wang, Kecheng Huang, Zili Shao
Institutions: Chinese University of Hong Kong, Shenzhen University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402806167