Abstract. The dramatic increase of daily usage of mobile devices generates massive digital footprints of users. Such footprints come from physical sensing such as GPS, WiFi, and Bluetooth, as well as social behavior sensing, e.g., call logs, application usage, etc. Many existing studies apply the mobile device footprints to infer daily activities like sitting/standing and social contexts such as personality traits and emotional states. In this paper, we propose a different approach to explore multimodal mobile footprints and build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user discriminatively. These descriptive features protect sensitive information, thus can be shared, transmitted, and reused with less privacy concerns. By testing on 22 users' mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification. In particular, our conditional entropy footprint statistics can achieve 81% accuracy across all 22 users while evaluating over 10-day intervals
