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.
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017).
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM 2018)
Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020).