Privacy through Solidarity: A User-Utility-Preserving Framework to Counter Profiling

Online service providers gather vast amounts of data to build user profiles. Such profiles improve service quality through personalization, but may also intrude on user privacy and incur discrimination risks. In this work, we propose a framework which leverages solidarity in a large community to scramble user interaction histories. While this is beneficial for anti-profiling, the potential downside is that individual user utility, in terms of the quality of search results or recommendations, may severely degrade. To reconcile privacy and user utility and control their trade-off, we develop quantitative models for these dimensions and effective strategies for assigning user interactions to Mediator Accounts. We demonstrate the viability of our framework by experiments in two different application areas (search and recommender systems), using two large datasets.