Work

Privacy preserving collaborative filtering for SaaS enabling PaaS clouds

Year: 2012

Type: article

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

Source: Journal of Cloud Computing Advances Systems and Applications

DOI: 10.1186/2192-113x-1-8

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

Institutions: Tokai University, Rutgers, The State University of New Jersey

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790