Title :
Privacy Preserving Search Schemes over Encrypted Cloud Data: A Comparative Survey
Author :
Jian Shen;Dengzhi Liu;Jun Shen;Haowen Tan;Debiao He
Author_Institution :
Jiangsu Eng. Center of Network Monitoring, Nanjing, China
Abstract :
Due to the development of the network and the growth big data, data owner prefer to remotely outsource their data to cloud, which can avoid the local data management and decrease the local hardware cost. But some sensitive data, such as personal healthcare information and personal property information, must be encrypted firstly and then outsourced to the cloud. This can protect sensitive information. But the outsource encrypted data to cloud can increase the difficulty of the data retrieval, because data owner or unauthorized users can´t search correctly the data they need, and also it is impractical to download all of the data to local side from the cloud, which will result in huge communication an computation overhead. Hence, some cloud data retrieval schemes has been proposed to solve this problem, these schemes not only can search the data they need correctly, but also can prevent sensitive data leaked out. In this paper, we survey the privacy preserving cloud data retrieval schemes and give a comparison of them with respect to the key principles of search and privacy assured.
Keywords :
"Cloud computing","Cryptography","Data privacy","Organizations","Computational modeling","Data models"
Conference_Titel :
Computational Intelligence Theory, Systems and Applications (CCITSA), 2015 First International Conference on
DOI :
10.1109/CCITSA.2015.46