Title :
Privacy-preserving Collaborative Filtering for the Cloud
Author :
Basu, Anirban ; Vaidya, Jaideep ; Kikuchi, Hiroaki ; Dimitrakos, Theo
Author_Institution :
Grad. Sch. of Eng., Tokai Univ., Tokyo, Japan
fDate :
Nov. 29 2011-Dec. 1 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.
Keywords :
cloud computing; collaborative filtering; data privacy; Google App Engine for Java cloud platform; generalised problem; privacy preserving schemes; privacy-preserving collaborative filtering; public cloud computing platforms; rating-based collaborative filtering; Cloud computing; Collaboration; Encryption; Privacy; Vectors; cloud computing; homomorphic encryption; privacy-preserving collaborative filtering; slope one;
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on
Conference_Location :
Athens
Print_ISBN :
978-1-4673-0090-2
DOI :
10.1109/CloudCom.2011.38