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.

翻译：尽管在保护数据隐私方面做出了巨大努力,但长期以来一直被忽视的一个重要问题是,数据流动中传输的数据可能会泄漏数据中未明确包含的其他受管制的信息,从而导致信息泄漏风险不为人知。为了在实际数据传输之前可靠地披露这种风险,我们提议FIAT,即无信托跨境数据流动的精细信息审计系统,这是无信托跨境数据流动的精细信息审计系统。在FIAT中,我们采用学习方法量化信息泄漏的数量,同时运用零知识证明和智能合同技术提供可信和保密的审计结果。实验表明,大量信息泄漏能够利用简单的机器学习模型提高未输入信息的可预测性,揭示信息审计的可变性。进一步验证国际财联的绩效。