Summary
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-respecting 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 for the smartphone and smart home environments and wrote three example collectors to enhance existing 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.
Code on GitHub
Coming Soon!
Research Paper
When referring to our work, please cite it as:
Amir Rahmati, Earlence Fernandes, Kevin Eykholt, Xinheng Chen, and Atul Prakash
Heimdall: A Privacy-Respecting Implicit Preference Collection Framework
15th ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys 2017), June 2017
or, use BibTeX for citation:
@InProceedings{heimdall17,
author = {Amir Rahmati and Earlence Fernandes and Kevin Eykholt and Xinheng Chen and Atul Prakash},
title = {{Heimdall: A Privacy-Respecting Implicit Preference Collection Framework}},
booktitle = {15th ACM International Conference on Mobile Systems, Applications, and Services},
month = June,
year = 2017
}
Team
Amir Rahmati, Ph.D. Candidate, University of Michigan
Earlence Fernandes, Ph.D. Candidate, University of Michigan
Kevin Eykholt, Ph.D. Candidate, University of Michigan
Xinheng Chen, Student, University of Michigan
Atul Prakash, Professor, University of Michigan
Acknowledgements
