Anonymity and Security in Genomic Datasets
Project Members
Kristine Arthur, Dr. John A. Springer, Dr. Melissa J. Dark
Kristine Arthur, Dr. John A. Springer, Dr. Melissa J. Dark
Abstract
Currently, genomic datasets are used in a variety of research applications. For example, findings in pharmacogenomics have provided more accurate prescription drug dosages and resulted in lower healthcare costs. However, the privacy of individuals included within datasets is a critical and challenging issue. It is possible to link an individual's identity to information out of a dataset. When a sequence is known, information such as disposition to certain diseases and disorders can be learned. This information could then be used to deny health cover, affect consideration for employment, or increase healthcare costs. Additionally, this information could be used to adversely affect other biological family members, such as children. While there have been attempts such as the Health Insurance Portability and Accountability Act (HIPAA) and the Genetic Information Nondiscrimination Act of 2008 (GINA), these measure alone are insufficient to guarantee protection. An examination of the literature shows that there is very little research done to develop methods of protecting genomic data. It is the aim of this project to identify best practices from currently existing privacy methods.