scholarly journals Conjunctive, Subset, and Range Queries on Encrypted Data

2007 ◽  
pp. 535-554 ◽  
Author(s):  
Dan Boneh ◽  
Brent Waters
Keyword(s):  
Range Queries ◽  
Encrypted Data

Privacy Protection in Outsourced Spatial Databases

2016 ◽  
Vol 10 (03) ◽  
pp. 347-363
Author(s):  
Ibrahim Kamel ◽  
Mohammed N. Ba-Hutair
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Range Queries ◽  
Security Issue ◽  
Simulation Experiments ◽  
Encrypted Data ◽  
Space Filling Curve ◽  
Data Objects ◽  
Filling Curve ◽  
Entire Dataset

Organizations have come to realize that storing their databases in the Cloud rather than in-house data centers is cheaper and more flexible. However, companies are still concerned about the privacy and the security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the entire dataset, which is impractical. This paper proposes a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. Data objects are encrypted and sorted using Z-order space-filling curve. An index is built on top of the encrypted data to be utilized by the Service Provider to identify and retrieve a superset of data objects that contains the answers to the query. Many simulation experiments have been carried out to measure the performance of the proposed scheme in terms of the redundancy in data retrieved. The experimental results show that the proposed scheme outperforms the most recent scheme by Kim et al. in terms of data redundancy.

A New Scheme for Range Queries over Encrypted Data

2014 ◽  
Vol 9 (11) ◽  
Author(s):  
Shanyue Bu ◽  
Yue Zhang ◽  
Kun Yu
Keyword(s):  
Range Queries ◽  
Encrypted Data

Range Queries over Encrypted Data

2020 ◽  
pp. 3-36
Author(s):  
Alex X. Liu ◽  
Rui Li
Keyword(s):  
Range Queries ◽  
Encrypted Data

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.

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
Sign in / Sign up

Export Citation Format

Share Document