CS700:Graduate Seminar in Computer Science & Informatics

Integrating Formalism and Pragmatism to Build Real Data Privacy Solutions

Organizations collect personal information while providing an ever-expanding set of services. The collected data can support various "secondary" uses and to protect privacy, various law requires information be rendered "de-identified" before it is repurposed. However, a growing body of evidence suggests data can be "re-identified" and the viability of such laws have been questioned. I will review how and why re-identification occurs, but also push the discussion from a deterministic view (i.e., re-identification can or cannot occur) toward a probabilistic view (i.e., the likelihood of re-identification). In doing so, I will illustrate how to construct efficient de-identification algorithms that mitigate risks without precluding the secondary endeavors. This work will draw upon experience and case studies from with multiple medical centers around the United States.
Brad Malin is an Assistant Professor of Biomedical Informatics and Computer Science at Vanderbilt University, where he directs the Health Information Privacy Laboratory (HIPLab). The HIPLab is funded through grants from the NSF and NIH and its research artifacts have received awards of distinction from the American and International Medical Informatics Associations. In 2010, Dr. Malin received the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the U.S. government on outstanding scientists and engineers beginning their independent careers. He completed his education at Carnegie Mellon University, where he received a bachelor's in biological sciences, a master's in data mining and knowledge discovery, a master's in public policy and management, and a doctorate in computer science.