USENIX Security ’23 – Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers
Noé Zufferey and Mathias Humbert, University of Lausanne, Switzerland; Romain Tavenard, University of Rennes, CNRS, LETG, France; Kévin Huguenin, University of Lausanne, Switzerland
Wearable devices, such as wearable activity trackers (WATs), are increasing in popularity. Although they can help to improve one’s quality of life, they also raise serious privacy issues. One particularly sensitive type of information has recently attracted substantial attention, namely personality, as it provides a means to influence individuals (e.g., voters in the Cambridge Analytica scandal). This paper presents the first empirical study to show a significant correlation between WAT data and personality traits (Big Five). We conduct an experiment with 200+ participants. The ground truth was established by using the NEO-PI-3 questionnaire. The participants’ step count, heart rate, battery level, activities, sleep time, etc. were collected for four months. By following a principled machine-learning approach, the participants’ personality privacy was quantified. Our results demonstrate that WATs data brings valuable information to infer the openness, extraversion, and neuroticism personality traits. We further study the importance of the different features (i.e., data types) and found that step counts play a key role in the inference of extraversion and neuroticism, while openness is more related to heart rate.
View the full USENIX Security ’23 program at https://www.usenix.org/conference/usenixsecurity23/program