Efficient Privacy-Preserving Range Queries over Encrypted Data in Cloud Computing

2013 ◽  
Author(s):  
Bharath K. Samanthula ◽  
Wei Jiang
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Range Queries ◽  
Encrypted Data

An Efficient Privacy-Preserving Rank Query over Encrypted Data in Cloud Computing

2012 ◽  
Vol 35 (11) ◽  
pp. 2215 ◽  
Author(s):  
Fang-Quan CHENG ◽  
Zhi-Yong PENG ◽  
Wei SONG ◽  
Shu-Lin WANG ◽  
Yi-Hui CUI
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Encrypted Data

Privacy-Preserving Keyword Search Over Encrypted Data in Cloud Computing

2013 ◽  
pp. 189-212 ◽  
Author(s):  
Wenhai Sun ◽  
Wenjing Lou ◽  
Y. Thomas Hou ◽  
Hui Li
Keyword(s):  
Cloud Computing ◽  
Keyword Search ◽  
Privacy Preserving ◽  
Encrypted Data

scholarly journals Classification and Privacy Preserving Search of Multimedia Data

2018 ◽  
Vol 7 (3.34) ◽  
pp. 259
Author(s):  
J Jeejo Vetharaj ◽  
S Selvanayaki ◽  
M B.Suseela
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Classification Problem ◽  
Privacy Preserving ◽  
Multimedia Data ◽  
Relational Data ◽  
Encrypted Data ◽  
Classification Techniques ◽  
Security Models ◽  
Privacy Issues

Classification, which is commonly used task in data mining applications separates the data present in the database based on some category. For years and years, considering the rise of several privacy issues, solutions in the form of theoretical and practical have been proposed for the classification problem under various security models. However, for the late Notoriety about cloud computing, clients presently have the chance on outsource their data, clinched alongside encrypted form, and also those information mining assignments of the cloud.. The data on the cloud which is in encrypted form, therefore existing privacy preserving classification techniques are not applicable. In this paper, we focus on finding solution for the classification problem over the encrypted data .Users can store their data with encryption by the use of ordered relational data. So, the data is obtained correctly without decrypting. 

scholarly journals Enhancing Privacy Preserving Query Retrieval Using Locality Sensitive Hashing

2019 ◽  
pp. 70-73
Author(s):  
Archana M.S. ◽  
K. Deepa
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Locality Sensitive Hashing ◽  
Range Queries ◽  
Potential Threat ◽  
Encrypted Data ◽  
Points Of Interest ◽  
Spatial Range ◽  
Day By Day

The usage of smart phones is tremendously increasing day by day. Due to this, Location Based Services (LBS) attracted considerably and becomes more popular and vital in the area of mobile applications. On the other hand, the usage of LBS leads to potential threat to user’s location privacy. In this paper, the famous LBS provide information about points of interest (POI) in spatial range query within a given distance. For that, a more efficient and an enhanced privacy-preserving query solution for location based, Efficient Privacy-Location Query (EPLQ) is proposed along with Locality Sensitive Hashing (LSH) reduces the dimensionality of high dimensional data. Experiments are conducted extensively and the results show the efficiency of the proposed algorithm EPLQ in privacy preserving over outsourced encrypted data in spatial range queries. The proposed method performs in spatial range queries and similarity queries of privacy preserving.

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