Abstract
Startups and large technology companies are working with companies in healthcare to research, create, and deploy machine learning healthcare solutions. The growth of machine learning healthcare solutions is increasing the risk of re-identification of health data, raising concerns for individual privacy. Differential privacy is one of the latest and most popular anonymization techniques used on machine learning data to guarantee data privacy but is presenting challenges when applied to health data. The Health Insurance Portability and Accountability Act (HIPAA) has loopholes and does not address the use of machine learning on health data. This paper will explain why HIPAA needs to be amended to reduce the risk of re-identification due to the growth of machine learning in healthcare and the challenges presented in applying differential privacy. The paper will also discuss three possible proposals to amend HIPAA to reduce the risk of re-identification.
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