GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)-猫眼课题宝

DOI：

10.5281/zenodo.7089491

发表时间：

2022-11

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

E. Jefferson;J. Liley;Maeve Malone;S. Reel;Alba Crespi-Boixader;X. Kerasidou;Francesco Tava;Andrew McCarthy;R. Preen;Alberto Blanco-Justicia;Esma Mansouri-Benssassi;J. Domingo-Ferrer;J. Beggs;Antony Chuter;Christian Cole;F. Ritchie;A. Daly;Simon Rogers;Jim Q. Smith

TREs被广泛并越来越多地用于支持对一系列部门（例如卫生、警察、税务和教育）的敏感数据进行统计分析，因为它们能够在保护数据机密性的同时实现安全和透明的研究。学术界和工业界越来越希望在TREs中训练人工智能模型。人工智能领域正在迅速发展，其应用包括发现人为错误、简化流程、任务自动化和决策支持。这些复杂的人工智能模型需要更多的信息来描述和复制，这增加了从这些描述中推断出敏感个人数据的可能性。TREs没有成熟的流程和对这些风险的控制。这是一个复杂的话题，期望所有的研究人员都意识到所有的风险，或者期望研究人员在人工智能特定的培训中解决这些风险是不合理的。gramatter已经为TREs开发了一套可用的建议草案，以防止在从TREs中披露经过训练的人工智能模型时出现额外的风险。这些建议的发展是由GRAIMATTER UKRI DARE英国冲刺研究项目资助的。我们的建议版本于2022年9月在项目结束时发布。在项目过程中，我们确定了许多领域，以便在实践中扩展和测试这些建议。因此，我们期望本文档将随着时间的推移而发展。

TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure and transparent research whilst protecting data confidentiality. There is an increasing desire from academia and industry to train AI models in TREs. The field of AI is developing quickly with applications including spotting human errors, streamlining processes, task automation and decision support. These complex AI models require more information to describe and reproduce, increasing the possibility that sensitive personal data can be inferred from such descriptions. TREs do not have mature processes and controls against these risks. This is a complex topic, and it is unreasonable to expect all TREs to be aware of all risks or that TRE researchers have addressed these risks in AI-specific training. GRAIMATTER has developed a draft set of usable recommendations for TREs to guard against the additional risks when disclosing trained AI models from TREs. The development of these recommendations has been funded by the GRAIMATTER UKRI DARE UK sprint research project. This version of our recommendations was published at the end of the project in September 2022. During the course of the project, we have identified many areas for future investigations to expand and test these recommendations in practice. Therefore, we expect that this document will evolve over time.

GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)

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