The privacy risks of compiling mobility data

A new study by MIT researchers finds that the growing practice of compiling massive, anonymized datasets about people’s movement patterns is a double-edged sword: While it can provide deep insights into human behavior for research, it could also put people’s private data at risk. Companies, researchers, and other entities are beginning to collect, store, and process anonymized data that contains “location stamps” (geographical coordinates and time stamps) of users. Data can be grabbed from mobile phone records, credit card transactions, public transportation smart cards, Twitter accounts, and mobile apps. Merging those datasets could provide rich information about how humans travel, for instance, to optimize transportation and urban planning, among other things. But with big data come big privacy issues: Location stamps are extremely specific to individuals and can be used for nefarious purposes. Recent research has shown that, given only a few randomly selected points in mobility datasets, someone could identify and learn sensitive information about individuals. With merged mobility datasets, this becomes even easier: An agent could potentially match users trajectories in anonymized data from one dataset, with deanonymized data in another, to unmask the anonymized data.