Integrating User Attribute Influences and DL-Based Anonymization for Enhanced Privacy Protection in Medical Record Data Sharing for Publishing
Keywords:
Privacy protection, Quasi-identifiers, Sensitive attributes, Sanitization, UtilityAbstract
In today's data-driven healthcare research landscape, sharing medical records for research is pivotal for advancing medical knowledge and patient care. However, ensuring individuals' privacy while maintaining data utility poses a significant challenge. To tackle this issue, this study proposes a novel Attribute Influence Anonymization using RVAE (AIARVAE) for enhancing both privacy and utility in medical records data sharing. The proposed model employs a preprocessing step to identify and filter Quasi-Identifiers (QIs) and Sensitive Attributes (SAs) from the dataset. Then quantify the susceptibility of QIs and measure the uncertainty of SAs using entropy. These metrics are then fed into a Recurrent Variational Auto-Encoder (RVAE) model, which replaces low-entropy SAs with sanitized values with the help of QI values. This approach mitigates the risk of explicit disclosure of private information while preserving data utility. By integrating attribute influences, the proposed model provides a comprehensive solution for safeguarding medical records data privacy during research sharing and promoting responsible and ethical data-driven healthcare research.
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