Work

Committed random double blinded coalition-proofed sampling

Year: 2025

Type: article

Abstract: The digital economy, including data trading, auditing, and trust, is an essential and rapidly growing field. A secure and committed data sampling process is necessary for those processes. We introduce a novel committed random double-blind sampling methodology for data auditing and transactions, which utilizes cryptography and blockchain technologies. This approach ensures that the sampler only has access to the sampled data. The sampling method we propose is also double-blind, meaning that neither the sampler nor the data owner can independently determine the positions of the sampled data. Instead, they are jointly decided by both parties. Additionally, the method permits the data sampler to detect if the data owner has intentionally chosen high-quality data or provided data extraneous to the data set. This innovative methodology guarantees that the data sampling process is both trustworthy and traceable. We supply a security analysis and offer solutions for various scenarios, such as multi-file and three-party sampling. We also present a sampling process designed to prevent collusion when sampling occurs among three parties.

Source: Journal of Digital Economy

DOI: 10.1016/j.jdec.2025.05.007

Authors: Shengzhe Meng, Jintai Ding

Institutions: Tsinghua University, Xi’an Jiaotong-Liverpool University

Language: English

Topic: Cryptography and Data Security

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: hybrid

Estimated APC paid: $500