Privacy Policy and Technology in Biomedical Data Science

Posted: 6 Feb 2019

See all articles by April Moreno Arellano

April Moreno Arellano

University of California, San Diego (UCSD) - School of Medicine

Wenrui Dai

University of California, San Diego (UCSD) - School of Medicine

Shuang Wang

University of California, San Diego (UCSD)

Xiaoqian Jiang

University of California, San Diego (UCSD)

Lucila Ohno-Machado

University of California, San Diego (UCSD)

Date Written: July 2018

Abstract

Privacy is an important consideration when sharing clinical data, which often contain sensitive information. Adequate protection to safeguard patient privacy and to increase public trust in biomedical research is paramount. This review covers topics in policy and technology in the context of clinical data sharing. We review policy articles related to ( a) the Common Rule, HIPAA privacy and security rules, and governance; ( b) patients’ viewpoints and consent practices; and ( c) research ethics. We identify key features of the revised Common Rule and the most notable changes since its previous version. We address data governance for research in addition to the increasing emphasis on ethical and social implications. Research ethics topics include data sharing best practices, use of data from populations of low socioeconomic status (SES), recent updates to institutional review board (IRB) processes to protect human subjects’ data, and important concerns about the limitations of current policies to address data deidentification. In terms of technology, we focus on articles that have applicability in real world health care applications: deidentification methods that comply with HIPAA, data anonymization approaches to satisfy well-acknowledged issues in deidentified data, encryption methods to safeguard data analyses, and privacy-preserving predictive modeling. The first two technology topics are mostly relevant to methodologies that attempt to sanitize structured or unstructured data. The third topic includes analysis on encrypted data. The last topic includes various mechanisms to build statistical models without sharing raw data.

Suggested Citation

Arellano, April Moreno and Dai, Wenrui and Wang, Shuang and Jiang, Xiaoqian and Ohno-Machado, Lucila, Privacy Policy and Technology in Biomedical Data Science (July 2018). Annual Review of Biomedical Data Science, Vol. 1, pp. 115-129, 2018, Available at SSRN: https://ssrn.com/abstract=3329797 or http://dx.doi.org/10.1146/annurev-biodatasci-080917-013416

April Moreno Arellano (Contact Author)

University of California, San Diego (UCSD) - School of Medicine ( email )

9500 Gilman Drive
MC 0507
La Jolla, CA 92093
United States

Wenrui Dai

University of California, San Diego (UCSD) - School of Medicine

9500 Gilman Drive
MC 0507
La Jolla, CA 92093
United States

Shuang Wang

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

Xiaoqian Jiang

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

Lucila Ohno-Machado

University of California, San Diego (UCSD) ( email )

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

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