Sihem Bouhenniche (University of Lille - Inria)
My name is Sihem Bouhenniche. I am currently pursuing a PhD. in cybersecurity at the University of Lille, with a focus on user privacy protection. My research centers around privacy and security issues related to mobile devices, particularly Android device fingerprinting.
I am also a member of the Spirals research team at Inria Lille. Before starting my PhD, I worked with the team for two years as a research engineer. During that time, I contributed to the development of amiunique.org, a popular browser fingerprinting platform that accounts around 2,000 visits per day.
I graduated from the Higher School of Computer Science of Algiers (ESI - Oued Smar) with both a Software Engineering degree and a Master’s degree. I also worked as a frontend developer at Ouedkniss.com, the largest e-commerce platform in Algeria, where I helped redesign the platform's interface and contributed to various new projects.
Sessions
Android is the dominant mobile operating system, powering more than 70% of the global mobile market and presenting a significant opportunity for user tracking. As privacy regulations tighten around how personal data can be used and collected, trackers are looking for alternatives that are under less scrutiny to evade detection. Device fingerprinting has emerged as a key solution, allowing trackers to create identifiers without user consent in a stealthy manner. Despite the extensive research on fingerprinting done from a web browser in the past decade, device fingerprinting on Android remains relatively understudied, with limited literature exploring its specific techniques and implications for user privacy.
In this study, we introduce EXADPrinter, a novel exhaustive permissionless device fingerprinting framework targeting Android devices. Without requiring permissions, our framework extracts over 200,000 properties per device by leveraging methods such as Java reflection and execution of shell commands. Through a dedicated Android application and a 6-month data collection, we gathered over 1151 fingerprints coming from 833 different Android devices, covering 41 manufacturers and 7 Android versions ranging from 9 to 15.
Through our framework, we demonstrate that diverse data can be collected about the device hardware, the operating system running on it, and the user, without requiring special permissions. We show that combining a few attributes without any IDs or personal information is enough to uniquely identify each device of our dataset, painting a bleak picture of the current state of the Android ecosystem.
Moreover, our framework highlights the negative impact of custom operating systems and manufacturer-specific customizations as they enhance the device fingerprinting effectiveness. Furthermore, EXADPrinter uncovers some leakage of sensitive information caused essentially by manufacturer customizations, including the exposure of user emails, emergency contacts, and persistent identifiers such as SIM identifiers.