Recommender systems use, amongst others, a mechanism called collaborative filtering (CF) to predict the rating that\r\na user will give to an item given the ratings of other items provided by other users. While reasonably accurate CF can\r\nbe achieved with various well-known techniques, preserving the privacy of rating data from individual users poses a\r\nsignificant challenge. Several privacy preserving schemes have, so far, been proposed in prior work. However, while\r\nthese schemes are theoretically feasible, there are many practical implementation difficulties on real world public\r\ncloud computing platforms. In this paper, we present our implementation experience and experimental results on\r\ntwo public Software-as-a-Service (SaaS) enabling Platform-as-a-Service (PaaS) clouds: the Google App Engine for Java\r\n(GAE/J) and the Amazon Web Services Elastic Beanstalk (AWS EBS).a
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