Privacy-Preserving Access to Big Data in the Cloud

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

doi:10.1109/MCC.2016.107

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 journal › Article (for policy / professional audience) › Academic research › peer-review

28 Citations (Scopus)

Abstract

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 language	English
Pages	34-42
Number of pages	9
Volume	3
No.	5
Specialist publication	IEEE Cloud Computing
DOIs	https://doi.org/10.1109/MCC.2016.107
Publication status	Published - 1 Jan 2016

Keywords

cloud computing
load balancing
oblivious RAM
privacy
storage
Survey

ASJC Scopus subject areas

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

More information

10.1109/MCC.2016.107

Cite this

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KW - storage

KW - Survey

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Privacy-Preserving Access to Big Data in the Cloud

Abstract

Keywords

ASJC Scopus subject areas

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