  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 Audit system
for Trustless transborder data flow. In FIAT, we use a learning approach to
quantify the amount of information leakage, while the technologies of
zero-knowledge proof and smart contracts are applied to provide trustworthy and
privacy-preserving auditing results. Experiments show that large information
leakage can boost the predictability of uninvolved information using simple
machine-learning models, revealing the importance of information auditing.
Further performance benchmarking also validates the efficiency and scalability
of the FIAT auditing system.
