Privacy preserving collaborative filtering for SaaS enabling PaaS clouds

Anirban Basu, Jaideep Vaidya, Hiroaki Kikuchi, Theo Dimitrakos, Srijith K. Nair

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Recommender systems use, amongst others, a mechanism called collaborative filtering (CF) to predict the rating that a user will give to an item given the ratings of other items provided by other users. While reasonably accurate CF can be achieved with various well-known techniques, preserving the privacy of rating data from individual users poses 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 present our implementation experience and experimental results on two public Software-as-a-Service (SaaS) enabling Platform-as-a-Service (PaaS) clouds: the Google App Engine for Java (GAE/J) and the Amazon Web Services Elastic Beanstalk (AWS EBS).a.

Original languageEnglish (US)
Article number8
Pages (from-to)1-14
Number of pages14
JournalJournal of Cloud Computing
Volume1
Issue number1
DOIs
StatePublished - 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Cloud computing
  • Collaborative filtering
  • Homomorphic cryptosystem
  • Privacy
  • Slope one

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