Artificial Intelligence to Support Biomedical Analytics and Data Privacy at Scale
Bradley Malin
May 18, 2022, Wednesday, 3:00 PM - 4:00 PM EDT
Privacy is a social construct that is realized in different ways under varying situations in healthcare and biomedical research. In this respect, context is king, such that the manner by which privacy can be injected into a system is dependent on a variety of factors that influence the environment. This is particularly the case when considering privacy in the big data age or what one might call big privacy. As computing becomes increasingly cheap and ever more ubiquitous, it seems as though upholding privacy is an impossible task. This notion is supported by the development and demonstration of a growing array of attacks on certain types of protections biomedical data managers aim to inject into clinical and genomic data shared for various purposes, such as the obfuscation of a patient's identity or the suppression of sensitive facts about a research participant or academic medical center. At the same time, these methodologies make strong assumptions about the extent to which an adversary functions in the world, such as operating under no (or limited) constraints with respect to resources at their disposal and motivation for mounting an attack. In this presentation, I will review attacks on biomedical data as they have evolved over the past several decades, posit a new approach to assessing data privacy risk in the real world that builds on computational economic perspectives of risk assessment, and introduce artificial data generation methods base on adversarial learning methods. To illustrate the potential for this approach, I will draw upon several examples of how we have applied these methods to sharing demographic, clinical, and genomic data, both at the individual- and summary-level for several U.S.-based consortia and multinational clinical trials.
Bradley Malin, Ph.D., is the Accenture Professor of Biomedical Informatics, Biostatistics, and Computer Science. He is also the Vice Chair for Research Affairs in the Department of Biomedical Informatics. His research is funded through various grants from the National Science Foundation (NSF), National Institutes of Health (NIH), and Patient Centered Outcomes Research Institute (PCORI) to construct technologies that enable artificial intelligence and machine learning applications (AI/ML) in the context of real world organizational, political, and health information architectures. To build practical solutions, his work draws upon methodologies in computer science, biomedical science, and public policy to innovate novel computational techniques. He has made specific contributions to a number of health-related areas, including distributed data processing methods for medical record linkage and predictive modeling, intelligent auditing technologies to protect electronic medical records from misuse in the context of primary care, and algorithms to formally anonymize patient information disseminated for secondary research purposes. Notably, his investigations on the empirical risks to health information re-identification have been cited by the Federal Trade Commission in the Federal Register and certain privacy enhancing technologies he developed have been featured in popular media outlets and blogs, including Nature News, Scientific American, and Wired magazine.

He co-directs the Health Data Science (HEADS) Center, the Center for Genetic Privacy and Identity in Community Settings (GetPreCiSe), an NIH Center of Excellence on Ethical, Legal, and Social Implications Research, and the Infrastructure Core of the NIH Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD). In addition he currently serves as the co-chair of the Committee on Access, Privacy, and Security (CAPS) of the All of Us Research Program of the U.S. Precision Medicine Initiative, an appointed member of the Technical Anonymisation Group of the European Medicines Agency, and an appointed member of the Board of Scientific Counselors of the National Center for Health Statistics of the Centers for Disease Control and Prevention.

He is an elected fellow of the National Academy of Medicine (NAM), the American College of Medical Informatics (ACMI), the International Academy of Health Sciences Informatics (IAHSI), and the American Institute for Medical and Biological Engineering (AIMBE). In addition, he was honored as a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House.

Dr. Malin completed his education at Carnegie Mellon University, where he received a bachelor's in biological sciences, a master's in machine learning, a master's in public policy and management, and a doctorate in computer science (with a focus on databases and software systems).