Efficient privacy-preserving keyword search method for retrieving data from cloud

Author(s):  
R. Brindha ◽  
A. Ghousia Samrin
2016 ◽  
Vol 27 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Chi Chen ◽  
Xiaojie Zhu ◽  
Peisong Shen ◽  
Jiankun Hu ◽  
Song Guo ◽  
...  

Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.


Author(s):  
Yinbin Miao ◽  
Ximeng Liu ◽  
Kim-Kwang Raymond Choo ◽  
Robert H. Deng ◽  
Jiguo Li ◽  
...  

2014 ◽  
Vol 25 (3) ◽  
pp. 48-71 ◽  
Author(s):  
Stepan Kozak ◽  
David Novak ◽  
Pavel Zezula

The general trend in data management is to outsource data to 3rd party systems that would provide data retrieval as a service. This approach naturally brings privacy concerns about the (potentially sensitive) data. Recently, quite extensive research has been done on privacy-preserving outsourcing of traditional exact-match and keyword search. However, not much attention has been paid to outsourcing of similarity search, which is essential in content-based retrieval in current multimedia, sensor or scientific data. In this paper, the authors propose a scheme of outsourcing similarity search. They define evaluation criteria for these systems with an emphasis on usability, privacy and efficiency in real applications. These criteria can be used as a general guideline for a practical system analysis and we use them to survey and mutually compare existing approaches. As the main result, the authors propose a novel dynamic similarity index EM-Index that works for an arbitrary metric space and ensures data privacy and thus is suitable for search systems outsourced for example in a cloud environment. In comparison with other approaches, the index is fully dynamic (update operations are efficient) and its aim is to transfer as much load from clients to the server as possible.


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