FIAT: Fine-grained Information Audit for Trustless Transborder Data Flow
Shuhao Zheng, Yanxi Lin, Yang Yu, Ye Yuan, Yongzheng Jia, Xue Liu

TL;DR
FIAT introduces a trustless, fine-grained auditing system that quantifies information leakage during transborder data flow using machine learning, zero-knowledge proofs, and smart contracts, addressing key privacy and trust challenges.
Contribution
The paper presents a novel system combining learning-based leakage quantification with zero-knowledge proofs for trustless auditing of sensitive data flows.
Findings
Large information leakage increases predictability of uninvolved data
FIAT is efficient and scalable for real-world applications
Trustless auditing ensures privacy and compliance in data transfer
Abstract
Auditing the information leakage of latent sensitive features during the transborder data flow has attracted sufficient attention from global digital regulators. However, there is missing a technical approach for the audit practice due to two technical challenges. Firstly, there is a lack of theory and tools for measuring the information of sensitive latent features in a dataset. Secondly, the transborder data flow involves multi-stakeholders with diverse interests, which means the audit must be trustless. Despite the tremendous efforts in protecting data privacy, an important issue that has long been neglected is that the transmitted data in data flows can leak other regulated information that is not explicitly contained in the data, leading to unaware information leakage risks. To unveil such risks trustfully before the actual data transfer, we propose FIAT, a Fine-grained Information…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Adversarial Robustness in Machine Learning · Blockchain Technology Applications and Security
