When geo-text meets security: privacy-preserving boolean spatial keyword queries
conference contribution
posted on 2019-01-01, 00:00authored byN Cui, Jianxin Li, X Yang, B Wang, M Reynolds, Yong XiangYong Xiang
In recent years, spatial keyword query has attracted wide-spread research attention due to the popularity of the location-based services. To efficiently support the online spatial keyword query processing, the data owners need to outsource their data and the query processing service to cloud platforms. However, the outsourcing services may raise privacy leaking issues because the cloud server on the platforms may not be trusted for both data owners and query users. Therefore, in this work, we first propose and formalize the problem of privacy-preserving boolean spatial keyword query under the widely accepted Known Background Thread Model. And then, we devise a novel privacy-preserving spatial-textual Bloom Filter encoding structure and an encrypted R-tree index. They can maintain both spatial and text information together in a secure way while answering the encrypted spatial keyword queries without the need for data decryption. To further accelerate the query processing, a compressed encrypted index is provided to deal with the challenges of the large dimension expansion and the expensive space consumption in the encrypted R-tree index. In addition, we develop the corresponding algorithms based on the designed index, and present the in-depth security analysis to show our work's satisfaction meeting the strong secure scheme. Finally, we demonstrate the performance of our proposed index and algorithms by conducting extensive experiments on four datasets under various system settings.
History
Pagination
1046-1057
Location
Macao, China
Start date
2019-04-08
End date
2019-04-11
ISSN
1084-4627
ISBN-13
9781538674741
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2019, IEEE
Editor/Contributor(s)
[Unknown]
Title of proceedings
ICDE 2019 : Proceedings of the 2019 IEEE 35th International Conference on Data Engineering