Latanya Sweeney is a computer scientist who is a Professor of Government and Technology in Residence at Harvard University, where she directs the Data Privacy Lab. Her work demonstrated, often dramatically, that removing names and addresses from a dataset does not make it anonymous.
In her widely cited paper “Simple Demographics Often Identify People Uniquely” (Carnegie Mellon, 2000), Sweeney analyzed 1990 US Census data and found that “87% (216 million of 248 million) of the population in the United States had reported characteristics that likely made them unique based only on {5-digit ZIP, gender, date of birth}.” To prove the point she famously bought Massachusetts voter records for twenty dollars and used them to re-identify supposedly anonymous hospital discharge data, including records of the state’s governor.
Sweeney is the originator of k-anonymity, a formal model requiring that each record in a released dataset be indistinguishable from at least k-1 others on identifying attributes. Her research reshaped how privacy law, public health, and data publishers think about de-identification, and it anticipated a long line of re-identification results, including the de-anonymization of the Netflix Prize dataset.