PrivacyCanary: Privacy-Aware Recommenders with Adaptive Input Obfuscation

Recommender systems are widely used by online retailers to promote products and content that are most likely to be of interest to a specific customer. In such systems, users often implicitly or explicitly rate products they have consumed, and some form of collaborative filtering is used to find other users with similar tastes to whom the products can be recommended. While users can benefit from more targeted and relevant recommendations, they are also exposed to greater risks of privacy loss, which can lead to undesirable financial and social consequences. The use of obfuscation techniques to preserve the privacy of user ratings is well studied in the literature. However, works on obfuscation typically assume that all users uniformly apply the same level of obfuscation. In a heterogeneous environment, in which users adopt different levels of obfuscation based on their comfort level, the different levels of obfuscation may impact the users in the system in a different way. In this work we consider such a situation and make the following contributions: (a) using an offline dataset, we evaluate the privacy-utility trade-off in a system where a varying portion of users adopt the privacy preserving technique. Our study highlights the effects that each user´s choices have, not only on their own experience but also on the utility that other users will gain from the system, and (b) we propose Privacy Canary, an interactive system that enables users to directly control the privacy-utility trade-off of the recommender system to achieve a desired accuracy while maximizing privacy protection, by probing the system via a private (i.e., undisclosed to the system) set of items. We evaluate the performance of our system with an off-line recommendations dataset, and show its effectiveness in balancing a target recommender accuracy with user privacy, compared to approaches that focus on a fixed privacy level.