A data recipient centered de-identification method to retain statistical attributes

Abstract

Privacy has always been a great concern of patients and medical service providers. As a result of the recent advances in information technology and the government's push for the use of Electronic Health Record (EHR) systems, a large amount of medical data is collected and stored electronically. This data needs to be made available for analysis but at the same time patient privacy has to be protected through de-identification. Although biomedical researchers often describe their research plans when they request anonymized data, most existing anonymization methods do not use this information when de-identifying the data. As a result, the anonymized data may not be useful for the planned research project. This paper proposes a data recipient centered approach to tailor the de-identification method based on input from the recipient of the data. We demonstrate our approach through an anonymization project for biomedical researchers with specific goals to improve the utility of the anonymized data for statistical models used for their research project. The selected algorithm improves a privacy protection method called Condensation by Aggarwal et al. Our methods were tested and validated on real cancer surveillance data provided by the Kentucky Cancer Registry.

Development and implementation of an instrument to assess privacy challenges of a web-based liver transplantation registry

“…The study, similar to other studies showed that anonymization, deleting or never using the identifiable elements such as the name or date of birth of patients' information, may greatly reduce their concerns about privacy (14,41). In contrast to a Cannoy and Salam qualitative study (43), the present quantitative study similar to other studies (42) demonstrated that the majority of patients are comfortable with sharing information among physicians.…”

Cancer registries and data protection in the age of health digital interoperability in Europe: The perspective of the Italian Network of Cancer Registries (AIRTUM)

“…Also, the sharing of good practices for data linkage within EU member states has been proposed ( 41 ). Although information technology has played a positive role in quality improvement and facility of cancer registries, more strict restriction strategies, such as identifying authentication levels, controlling and coding data approaches, tailored de-identification methods, and other technical measures, had to be developed to secure the patients’ privacy ( 22 , 42 , 43 ).…”

An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data

“…Another anonymization approach extends kanonymity and l-diversity models for anonymizing patient data with multiple sensitive attributes (Gal et al, 2008). Gal et al (2014) propose an anonymization framework which identifies a suitable de-identification method for cross-sectional data based on the requirements of the recipient of the data. Poulis et al (2017) propose anonymization of health data that contains both demographics and diagnoses codes.…”