Protecting Private Attributes in App Based Mobile User Profiling

Abstract

The Analytics companies enable successful targeted advertising via user profiles, derived from the mobile apps installed by specific users, and hence have become an integral part of the mobile advertising industry. This threatens the users' privacy, when profiling is based on apps representing sensitive information, e.g., gambling problems indicated by a game app. In this work, we propose an app-based profile obfuscation mechanism, ProfileGuard, with the objective of eliminating the dominance of private interest categories (i.e. the prevailing private interest categories present in a user profile). We demonstrate, based on wide-range experimental evaluation of Android apps in a nine month test campaign, that the proposed obfuscation mechanism based on similarity with user's existing apps (ensuring that selected obfuscating apps belong to non-private categories) can achieve a good trade-off between efforts required by the obfuscating system and the resulting privacy protection. We also show how the bespoke (customised to profile obfuscation) and bespoke++ (resource-aware) strategies can deliver significant improvements in the level of obfuscation and (particularly bespoke++) in the use of mobile resources, making the latter a good candidate strategy in resource-constrained scenarios e.g., for fixed data use mobile plans. We also implement a POC ProfileGuard app to demonstrate the feasibility of an automated obfuscation mechanism. Furthermore, we provide insights to Google AdMob profiling rules, such as showing how individual apps map to user's interests within their profile in a deterministic way and that AdMob requires a certain level of activity to build a stable user profile.

Keywords

• Privacy
• targeted ads
• mobile apps
• obfuscation
• user experience