BlurMe: Inferring and Obfuscating User Gender Based on Ratings.

User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. We design techniques for effectively adding ratings to a user’s profile for obfuscating the user’s gender, while having an insignificant effect on the recommendations provided to that user.