Fingerprinting and Personal Information Leakage from Touchscreen Interactions

Abstract

The study aims to understand and quantify the privacy threat landscape of touch-based biometrics. Touch interactions from mobile devices are ubiquitous and do not require additional permissions to collect. Two privacy threats were examined - user tracking and personal information leakage. First, we designed a practical fingerprinting simulation experiment and executed it on a large publicly available touch interactions dataset. We found that touch-based strokes can be used to fingerprint users with high accuracy and performance can be further increased by adding only a single extra feature. The system can distinguish between new and returning users with up to 75% accuracy and match a new session to the user it originated from with up to 74% accuracy. In the second part of the study, we investigated the possibility of predicting personal information attributes through the use of touch interaction behavior. The attributes we investigated were age, gender, dominant hand, country of origin, height, and weight. We found that our model can predict the age group and gender of users with up to 66% and 62% accuracy respectively. Finally, we discuss countermeasures, limitations and provide suggestions for future work in the field.