Towards the Analysis of How Anonymization Affects Usefulness of Health Data in the Context of Machine Learning

Fer Carmona, J. Conesa, Jordi Casas-Roma
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Abstract

The volume and quality of patient data stored and collected have drastically grown in the last years. Such data can be analyzed by machine learning algorithms to improve health and well-being. However, while the distribution of data is benefitial, it should be performed in a way that preserves patient privacy. It would be expected to obtain useful information from the use of machine learning algorithms applied to both anonymized and non-anonymized datasets. However, those algorithms can generate lower quality results (even invalid ones) due to information loss during the anonymization process. We aim to analyze the relationship between anonymization and data utility/information loss, through the use of different algorithms and information loss metrics. With that aim, we plan to 1) analyze how real algorithms used on real data are affected by different anonymization techniques; 2) to use the lessons learned to design useful metrics for measuring the information loss after annonymization; and 3) to validate the proposed metrics by testing them in other environments with different types of data. The expected contributions of the research will be to obtain more information about how anonymization techniques affect the data usefulness, together with additional knowledge about the more suitable machine learning algorithms to be used to anonymized data, and a set of metrics to measure the usefulness of anonymized data would be developed
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在机器学习的背景下,分析匿名化如何影响健康数据的有用性
存储和收集的患者数据的数量和质量在过去几年中急剧增长。这些数据可以通过机器学习算法进行分析,以改善健康和福祉。然而,虽然数据的分发是有益的,但它应该以保护患者隐私的方式进行。预计将从应用于匿名和非匿名数据集的机器学习算法的使用中获得有用的信息。然而,由于匿名化过程中的信息丢失,这些算法产生的结果质量较低(甚至无效)。我们的目标是通过使用不同的算法和信息丢失度量来分析匿名化与数据效用/信息丢失之间的关系。为此,我们计划1)分析不同匿名化技术对真实数据上使用的真实算法的影响;2)利用经验教训设计有用的指标来衡量匿名化后的信息损失;3)通过在其他环境中使用不同类型的数据进行测试来验证所提出的度量标准。该研究的预期贡献将是获得更多关于匿名化技术如何影响数据有用性的信息,以及关于更适合用于匿名化数据的机器学习算法的额外知识,并将开发一套衡量匿名数据有用性的指标
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