Privacy-Preserving Access to Big Data in the Cloud
Peng Li, Song Guo, Toshiaki Miyazaki, Miao Xie, Jiankun Hu, Weihua Zhuang · IEEE Cloud Computing · 2016
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.