Privacy Preserving Anonymization Techniques for Patient Data: An Overview
Paper ID : 1095-ICTCK (R1)
Manoj Jayabalan *, Muhammad Ehsan Rana, Mohung Abdoolah Aasif
Asia Pacific University of Technology & Innovation
Healthcare industry generates enormous amount of patients’ sensitive data in various forms. With the era of Big Data, the possibilities of gaining insights from the patient data can provide valuable service and discover groundbreaking health-related outcomes. By using this data, healthcare, pharmaceutical companies, and researchers will inevitably improve our quality of life through better decision making. However, protecting patient’s data has an utmost importance for the healthcare to safeguard the individual’s identity and hence the data integrity or utility must be preserved while publishing these datasets. Data publishers’ spend a considerable amount of time on anonymizing the data with different techniques to strike the balance between utility and privacy, since the quality of the result depends on the quality of data. This paper discusses the importance of preserving the privacy of individuals while publishing data without modifying the utility and provides a comparative study of different techniques. Finally, the different threats which can cause privacy issues on those techniques are discussed.
Anonymization, l-diversity, t-closeness, generalization, k-anonymity, Healthcare, Data Publishing, Privacy Preserving
Status : Paper Accepted