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

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