MIDAS Seminar Series Presents: Xiaoqian Jiang – University of Texas Health Science Center

Xiaoqian Jiang

Associate Professor, Biomedical Informatics, University of Texas Health Science Center

Director, Center for Secure Artificial Intelligence for Healthcare, University of Texas Health Science Center

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Genomic Data Sharing: the Privacy Risk and Technical Mitigations

Personalized medicine makes use of rich and multi-modality individual health data for the promise of better diagnosis, improved health, and a high quality and longer life. The human genome is a key piece in the puzzle. The collection and sharing of personal genomics for research alsobring an increasing concern about privacy, and the risk of discrimination and stigmatization. There are important ethical, legal, and social implications, for example, individual genome information is known to be uniquely identifiable, which is also highly associated with relatives. Trust, accountability, and equity are critical pillars to enable responsible data sharing.

In this talk, I will overview the genomic privacy risks to show various kinds of vulnerability, covering linkage attack, membership attack, and other inference attacks. Then, I will introduce some technical mitigation strategies including secure outsourcing, multiparty computing, and privacy-preserving output perturbation.  I hope that this talk will contribute to the awareness of our community with respect to the magnitude of the challenge and the necessity to develop effective and practical solutions.

Bio:

Dr. Jiang is a Christopher Sarofim family professor and center director of Secure Artificial intelligence For hEalthcare (SAFE) in the School of Biomedical Informatics (SBMI) at The University of Texas Health Science Center at Houston (UTHealth). Before joining UTHealth in 2018, he was an associate professor with tenure in the Department of Biomedical Informatics (DBMI) at UCSD. He is an associate editor of BMC Medical Informatics and Decision Making and serves as an editorial board member of the Journal of American Medical Informatics Association. His expertise is primarily in health data privacy and predictive models in biomedicine based on his Computer Science Ph.D. training from Carnegie Mellon University. He received NIH R00, R13, R21, R01, U01 grants as PI, obtained career awards like CPRIT Rising Stars and UT Stars, and won several best and distinguished paper awards from American Medical Informatics Association (AMIA) Joint Summits on Translational Science (2012, 2013, 2016). He is one of the organizers of the iDASH Genome Privacy competition (2014 – present), which was reported by Nature News and GenomeWeb.