 | This article includes a list of general references, but it lacks sufficient corresponding inline citations. (March 2021) |
Latanya Sweeney | |
|---|---|
 Sweeney at a panel discussion in New York City, November 2017 | |
| Education | |
| Known for | k-anonymity |
| Scientific career | |
| Fields | |
| Institutions | Harvard University Carnegie Mellon University |
| Thesis | Computational Disclosure Control: Theory and Practice (2001) |
| Website | latanyasweeney |
Latanya Arvette Sweeney is an American computer scientist. She is the Daniel Paul Professor of the Practice of Government and Technology at the Harvard Kennedy School and in the Harvard Faculty of Arts and Sciences at Harvard University.[1] She is the founder and director of the Public Interest Tech Lab, founded in 2021 with a $3 million grant from the Ford Foundation as well as the Data Privacy Lab.[2][3] She is the current Faculty Dean in Currier House at Harvard.[4][5]
Sweeney is the former Chief Technologist of the Federal Trade Commission and Editor-in-Chief of Technology Science.[6][7][8] Her best known academic work is on the theory of k-anonymity, and she is credited with the observation that "87% of the U.S. population is uniquely identified by date of birth, gender, postal code".[9]
- ^ "Latanya Sweeney". Harvard Kennedy School Faculty Profile. Retrieved 2021-06-21.
- ^ "People". Public Interest Tech Lab. Retrieved 2021-07-21.
- ^ "Humanizing Technology". Harvard Gazette. 23 June 2021. Retrieved 2021-07-22.
- ^ "Latanya Sweeney". Harvard Business School Digital Initiative. Retrieved 2019-11-12.
- ^ "Data Privacy Lab". dataprivacylab.org. Retrieved 2022-06-24.
- ^ "FTC Names Latanya Sweeney as Chief Technologist; Andrea Matwyshyn as Policy Advisor". Federal Trade Commission. November 18, 2013. Retrieved August 6, 2014.
- ^ "Hello world! | Federal Trade Commission". www.ftc.gov. Retrieved 2016-03-22.
- ^ "About Us | Technology Science". techscience.org. Retrieved 2022-06-24.
- ^ L. Sweeney. "Simple Demographics Often Identify People Uniquely (Data Privacy Working Paper 3) Pittsburgh 2000" (PDF). Carnegie Mellon University. Retrieved 18 January 2014.
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