Cross-device User Tracking via Hybrid Model

Abstract

With the integration of various smart devices (smartphones, laptops, desktops, etc.) into people’s lives, some traditional user tracking methods such as cookies, browser fingerprints, etc. have become ineffective. There is an urgent need for a new tracking paradigm that can track users across browsers and devices.In particular, we propose a new architecture for cross-device user tracking. We exploit the physical and behavioral characteristics of devices to discover more about users’ habits, build corresponding user-device pairs based on the fusion results to integrate the sub-models generated based on different feature lists. In addition, we implement a proof-of-concept prototype of the proposed method and evaluate it using real-world use cases. The information has been hashed to meet the user privacy requirements. Our evaluation results show that the scheme is feasible.