Privacy-Preserving Access to Big Data in the Cloud

Peng Li, Song Guo, Toshiaki Miyazaki, Miao Xie, Jiankun Hu, Weihua Zhuang

Research output: Publication in policy / professional / specialist journalArticle (for policy / professional audience)Academic researchpeer-review

21 Citations (Scopus)


Cloud storage can simplify data management and reduce data maintenance costs. However, many users and companies hesitate to move their data to cloud storage because of security and privacy concerns about third-party cloud service providers. Oblivious RAM (ORAM) aims to enable privacy-preserving access to data stored in the cloud. This article offers a tutorial on ORAM and surveys recent literature. The authors also study the access load-balancing problem when applying ORAM to big data in the cloud. They propose heuristic algorithms to achieve access load balancing in both static and dynamic deployments.
Original languageEnglish
Number of pages9
Specialist publicationIEEE Cloud Computing
Publication statusPublished - 1 Jan 2016


  • cloud computing
  • load balancing
  • oblivious RAM
  • privacy
  • storage
  • Survey

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Software
  • Computer Science Applications
  • Computer Networks and Communications


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