Not So Unique in the Crowd: a Simple and Effective Algorithm for Anonymizing Location Data

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 identifiable with only a few spatio-temporal points. We propose a modification-based anonymization approach that is based on shorting the trajectories to reduce the risk of reidentification and information disclosure. Empirical, experimental results on the anonymized dataset show the decrease of uniqueness and suggest that anonymization techniques can help to improve the privacy protection and reduce privacy risks, although the anonymized data cannot provide full anonymity so far.