Not So Unique in the Crowd: A Simple and Effective Algorithm for Anonymizing Location Data
The authors study the problem of privacy in human mobility data, i.e., the re-identification risk of individuals in a trajectory dataset. They quantify the risk of being re-identified by the metric of uniqueness, the fraction of individuals in the dataset which are uniquely identifiable by a set of spatio-temporal points. They explore a human mobility dataset for more than half a million individuals over a period of one week. The location of an individual is specified every fifteen minutes. The results show that human mobility traces are highly identifiable with only a few spatio-temporal points.