What your Fitbit Says about You: De-anonymizing Users in Lifelogging Datasets

Recently, there has been a significant surge of lifelogging experiments, where the activity of few participants\nis monitored for a number of days through fitness trackers. Data from such experiments can be aggregated\nin datasets and released to the research community. To protect the privacy of the participants, fitness datasets\nare typically anonymized by removing personal identifiers such as names, e-mail addresses, etc. However,\nalthough seemingly correct, such straightforward approaches are not sufficient. In this paper we demonstrate\nhow an adversary can still de-anonymize individuals in lifelogging datasets. We show that users’ privacy can\nbe compromised by two approaches: (i) through the inference of physical parameters such as gender, height,\nand weight; and/or (ii) via the daily routine of participants. Both methods rely solely on fitness data such as\nsteps, burned calories, and covered distance to obtain insights on the users in the dataset. We train several\ninference models, and leverage them to de-anonymize users in public lifelogging datasets. Between our two\napproaches we achieve 93.5% re-identification rate of participants. Furthermore, we reach 100% success rate\nfor people with highly distinct physical attributes (e.g., very tall, overweight, etc.).