Privacy preserving collaborative filtering for SaaS enabling PaaS clouds

Basu, Anirban, Vaidya, Jaideep, Kikuchi, Hiroaki, Dimitrakos, Theo, Nair, Srijith K. (2012) Privacy preserving collaborative filtering for SaaS enabling PaaS clouds. Journal of Cloud Computing: Advances, Systems and Applications, 1 (1). p. 8. ISSN 2192-113X. (doi:10.1186/2192-113X-1-8) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1186/2192-113X-1-8

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

Item Type: Article
DOI/Identification number: 10.1186/2192-113X-1-8
Additional information: <19> This paper is about experiences with implementing a cloud-based service for collaborative filtering services that allows user communities to rate and recommend concepts, services, transactions, etc., while preserving the privacy and secrecy of the interactions by leveraging partial homomorphic encryption. The implementation of these homomorphic encryption variants has been assessed over cloud platforms such as Google App Engine and Amazon WS (EBS). This extends the initial experiments presented in an IEEE CloudCom 2011 paper. Further experimental validation leading to efficiency improvements and specialisation in different cloud platform architectures is analysed in ACM SAC 2012 and IEEE Cloud 2013 papers.; number of additional authors: 4;
Uncontrolled keywords: Collaborative filtering; Privacy; Cloud computing; Homomorphic cryptosystem; Slope one
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunications
Divisions: Faculties > Sciences > School of Computing
Depositing User: Stewart Brownrigg
Date Deposited: 07 Mar 2014 00:05 UTC
Last Modified: 29 May 2019 12:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/40198 (The current URI for this page, for reference purposes)
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