When geo-text meets security: Privacy-preserving boolean spatial keyword queries

Ningning Cui, Jianxin Li, Xiaochun Yang, Bin Wang, Mark Reynolds, Yong Xiang

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1046-1057
Number of pages12
ISBN (Electronic)9781538674741
DOIs
Publication statusPublished - 1 Apr 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period8/04/1911/04/19

Fingerprint

Query processing
Location based services
Outsourcing
Servers
Experiments

Cite this

Cui, N., Li, J., Yang, X., Wang, B., Reynolds, M., & Xiang, Y. (2019). When geo-text meets security: Privacy-preserving boolean spatial keyword queries. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 1046-1057). [8731414] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2019.00097
Cui, Ningning ; Li, Jianxin ; Yang, Xiaochun ; Wang, Bin ; Reynolds, Mark ; Xiang, Yong. / When geo-text meets security : Privacy-preserving boolean spatial keyword queries. Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1046-1057 (Proceedings - International Conference on Data Engineering).
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abstract = "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.",
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Cui, N, Li, J, Yang, X, Wang, B, Reynolds, M & Xiang, Y 2019, When geo-text meets security: Privacy-preserving boolean spatial keyword queries. in Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019., 8731414, Proceedings - International Conference on Data Engineering, vol. 2019-April, IEEE, Institute of Electrical and Electronics Engineers, pp. 1046-1057, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 8/04/19. https://doi.org/10.1109/ICDE.2019.00097

When geo-text meets security : Privacy-preserving boolean spatial keyword queries. / Cui, Ningning; Li, Jianxin; Yang, Xiaochun; Wang, Bin; Reynolds, Mark; Xiang, Yong.

Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1046-1057 8731414 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).

Research output: Chapter in Book/Conference paperConference paper

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T1 - When geo-text meets security

T2 - Privacy-preserving boolean spatial keyword queries

AU - Cui, Ningning

AU - Li, Jianxin

AU - Yang, Xiaochun

AU - Wang, Bin

AU - Reynolds, Mark

AU - Xiang, Yong

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N2 - 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.

AB - 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.

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BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019

PB - IEEE, Institute of Electrical and Electronics Engineers

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Cui N, Li J, Yang X, Wang B, Reynolds M, Xiang Y. When geo-text meets security: Privacy-preserving boolean spatial keyword queries. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1046-1057. 8731414. (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2019.00097