Latanya Sweeney, Ph.D.

Academic Positions

• Visting Faculty, Harvard University, Computer Science, Center for Research on Computation and Society.
• Visting Professor, Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Lab (CSAIL).

• Associate Professor of Computer Science, Technology and Policy, Carnegie Mellon University, School of Computer Science, Institute for Software Research.
• Director, Laboratory for International Data Privacy, also known as the "Data Privacy Lab", School of Computer Science, Carnegie Mellon University.
• Co-Director, PhD Program in Computation, Organizations and Society, School of Computer Science, Carnegie Mellon University.
• Editor-in-Chief, Journal of Privacy Technology.

Other Academic Affiliations

• Faculty, Machine Learning Department, School of Computer Science, Carnegie Mellon University.
• Faculty, H. John Heinz III School of Public Policy and Management, Carnegie Mellon University.
• Faculty, Quality of Life Technology Center, Carnegie Mellon University and the University of Pittsburgh.
• Faculty, Aladdin Center, School of Computer Science, Carnegie Mellon.
• Faculty, Cylab, Carnegie Mellon University

Corporate Affiliations

No issued licenses limit my access to these technologies for academic purposes.
• Privacert, Inc. has licenses to some of my technologies. I maintain no ownership interest or hold any official position. I do maintain sufficient involvement to assure my technology is being properly deployed.
• CITC has licenses to some of my technologies. I maintain no ownership interest or hold any official position. I do maintain sufficient involvement to assure my technology is being properly deployed.
• Datanon, LLC had licenses to some of my technologies, which are now available through Privacert, Inc.

My overall research goal is to create systems that automatically learn strategic and sensitive information from data, and its converse, to create systems that control what can be learned. Most often, the sensitive information I want systems to learn are ways to relate personal identity to seemingly innocents facts ("re-identification" and "identifiability"). And most often my pursuits in the opposite direction involve creating systems with guarantees that identity cannot be learned ("anonymity") while still making sure the anonymized results remain useful.

My overall research agenda involves leveraging my work on identifiability and anonymity (1) to change the way a computer scientist or engineer thinks about privacy in the technology he creates; (2) to help establish the scientific study of privacy; and, (3) to help decision-makers weave technology and policy together to address today's privacy-technology conflicts.

View recent Research Projects or see old Research Artifacts.

Keywords: learning, data anonymity, privacy technology, re-identification, data linkage

Current Students (photos) Ralph Gross Xiaoqian Jiang Laurie Jones Yiheng Li Wanhong Xu

Past Students (photos and updates) Thesis Supervision (alphabetically): Samuel Edoho-Eket William ("Spike") Gronim Rolf Holzer Bradley Malin Brooke Singer Others: Edoardo Airoldi Aarthi Gopal Ponnurangam Kumaraguru William Malloy Elaine Newton Rebekah Siegel Ben Vernot Daniel Yurovsky

Current Collaborators (photos) Tom Hagan, Paradigm Health Andrew Halpert, Blue Cross Blue Shield David Hoffman, Intel Randy Miller, Vanderbilt University Kate Seelman, University of Pittsburgh Claire Vishik, Intel Vic Weedn, Allegheny County Forensic Lab Past Collaborators Joe Lombardo, Johns Hopkins University Hal Abelson, MIT Henry Leitner, Harvard University Peter Szolovits, MIT Jon Doyle ( now at NCSU) Gerald Sussman, MIT on amorphous computing

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