Feasibility of a privacy preserving collaborative filtering scheme on the Google App Engine - A performance case study

Anirban Basu, Jaideep Vaidya, Theo Dimitrakos, Hiroaki Kikuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

The cloud is a utility computing infrastructure that has caused a paradigm shift in the way organisations requisition, allocate, and use IT resources. One big challenge is to preserve the confidentiality of information on the cloud. Most typical solutions use cryptographic techniques without considering how well suited they are to the cloud. This paper presents a performance case-study on implementing the building blocks of a privacy preserving collaborative filtering (PPCF) scheme in Java on the Google App Engine (GAE/J) cloud platform. The results show that the GAE/J in its current state exhibits serious performance bottlenecks for the chosen application scenario. This case study highlights the need for better performance from the GAE/J. It also informs the need for validating theoretical cloud security algorithms on real cloud computing platforms in which many performance expectations do not hold.

Original languageEnglish (US)
Title of host publication27th Annual ACM Symposium on Applied Computing, SAC 2012
Pages447-452
Number of pages6
DOIs
StatePublished - 2012
Event27th Annual ACM Symposium on Applied Computing, SAC 2012 - Trento, Italy
Duration: Mar 26 2012Mar 30 2012

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Other

Other27th Annual ACM Symposium on Applied Computing, SAC 2012
Country/TerritoryItaly
CityTrento
Period3/26/123/30/12

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • cloud computing
  • cloud computing evaluation
  • performance
  • privacy

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