.png){kind=icon}
Return
Efficient Privacy-Preserving Spatial Data Query in Cloud Computing
.png){kind=icon}
OA
AI
DOI：10.1109/TKDE.2023.3283020.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$<^>+$+) by using Geohash-based $R$R-tree structure (called $GR$GR-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.
Keywords:
Cloud server
privacy-preserving
query range
security issues
spatial data
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
Cited Papers
Location Privacy-Preserving Task Recommendation With Geometric Range Query in Mobile Crowdsensing
.png){kind=icon}
PREAI
ShareSaveShareSaveLack of Ovarian Responsiveness to Gonadotropic Hormones in Infantile Rats Sterilized with Busulfan*
PREAI
ShareSavePystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-k Spatial-Textual Query
PREAI
ShareSaveShareSaveShareSave
View more