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Privacy-preserving collaborative filtering for the cloud

Anirban Basu, Jaideep Vaidya, Hiroaki Kikuchi and Theo Dimitrakos

In: The 3rd IEEE International Conference on Cloud Computing Technology and Science (Cloudcom), Athens, Greece.

Acceptance rate: 24%

Year: 2011

Abstract: Rating-based collaborative filtering (CF) enables the prediction of the rating that a user will give to an item, based on the ratings of other items given by other users. However, doing this while preserving the privacy of rating data from individual users is a significant challenge. Several privacy preserving schemes have, so far been proposed in prior work. However, while these schemes are theoretically feasible, there are many practical implementation difficulties on real world public cloud computing platforms. In this paper, we approach the generalised problem of privacy preserving collaborative filtering from the cloud perspective and propose an efficient and secure approach that is built for the cloud. We present our implementation experiences and experimental results based on the Google App Engine for Java (GAE/J) cloud platform.

Fulltext: PDF

Presentation: PDF

Demo: See http://gaejppcf.appspot.com (Google App Engine). Although originally planned to be used on the Google App Engine, hence the name 'gaejppcf' (for Google App Engine for Java -- Privacy Preserving Collaborative Filtering), we are also in the process of deploying it on Amazon Web Services. See http://gaejppcf.elasticbeanstalk.com (Amazon Elastic Beanstalk).
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