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Many of the everyday decisions a user makes rely on the suggestions of online recommendation systems. These systems amass implicit (e.g., location, purchase history, browsing history) and explicit (e.g., reviews, ratings) feedback from multiple users, produce a general consensus, and provide suggestions based on that consensus. However, due to privacy concerns, users are uncomfortable with implicit data collection, thus requiring recommendation systems to be overly dependent on explicit feedback. Unfortunately, users do not frequently provide explicit feedback. This hampers the ability of recommendation systems to provide high-quality suggestions. We introduce Heimdall, the first privacy-respecting implicit preference collection framework that enables recommendation systems to extract user preferences from their activities in a privacy respect- ing manner. The key insight is to enable recommendation systems to run a collector on a user’s device and precisely control the information a collector transmits to the recommendation system back- end. Heimdall introduces immutable blobs as a mechanism to guarantee this property. We implemented Heimdall on the Android plat- form and wrote three example collectors to enhance recommendation systems with implicit feedback. Our performance results suggest that the overhead of immutable blobs is minimal, and a user study of 166 participants indicates that privacy concerns are significantly less when collectors record only specific information—a property that Heimdall enables.