Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65577
Title: Privacy-preserving access to big data in the cloud
Authors: Li, P
Guo, S
Miyazaki, T
Xie, M
Hu, J
Zhuang, W
Keywords: Cloud computing
Load balancing
Oblivious RAM
Privacy
Storage
Survey
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE cloud computing, 2016, v. 3, no. 5, 7742270, p. 34-42 How to cite?
Journal: IEEE cloud computing 
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.
URI: http://hdl.handle.net/10397/65577
EISSN: 2325-6095
DOI: 10.1109/MCC.2016.107
Appears in Collections:Journal/Magazine Article

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