Not So Unique in the Crowd: a Simple and Effective Algorithm for Anonymizing Location Data
Song Yi, Daniel Dahlmeier, Stéphane Bressan · 2015
We study the problem of privacy in human mobility data, i.e., the re-identification risk of individuals in a trajectory dataset. We 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. We 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 iden-tifiable with only a few spatio-temporal points. We pro-pose a modification-based anonymization approach that is based on shorting the trajectories to reduce the risk of re-identification and information disclosure. Empirical, experi-mental results on the anonymized dataset show the decrease of uniqueness and suggest that anonymization techniques can help to improve the privacy protection and reduce pri-vacy risks, although the anonymized data cannot provide full anonymity so far. 1.