IEICE TRANSACTIONS on Information
A Semantic-Based Dual Location Privacy-Preserving Approach
Summary :
With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.5 pp.982-995
- Publication Date
- 2022/05/01
- Publicized
- 2022/02/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7185
- Type of Manuscript
- PAPER
- Category
- Information Network
Authors
Xudong YANG
Northwest University
Ling GAO
Northwest University
Yan LI
Northwest University
Jipeng XU
Northwest University
Jie ZHENG
Northwest University
Hai WANG
Northwest University
Quanli GAO
Northwest University
Keyword
Latest Issue
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Xudong YANG, Ling GAO, Yan LI, Jipeng XU, Jie ZHENG, Hai WANG, Quanli GAO, "A Semantic-Based Dual Location Privacy-Preserving Approach" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 982-995, May 2022, doi: 10.1587/transinf.2021EDP7185.
Abstract: With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7185/_p
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@ARTICLE{e105-d_5_982,
author={Xudong YANG, Ling GAO, Yan LI, Jipeng XU, Jie ZHENG, Hai WANG, Quanli GAO, },
journal={IEICE TRANSACTIONS on Information},
title={A Semantic-Based Dual Location Privacy-Preserving Approach},
year={2022},
volume={E105-D},
number={5},
pages={982-995},
abstract={With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.},
keywords={},
doi={10.1587/transinf.2021EDP7185},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - A Semantic-Based Dual Location Privacy-Preserving Approach
T2 - IEICE TRANSACTIONS on Information
SP - 982
EP - 995
AU - Xudong YANG
AU - Ling GAO
AU - Yan LI
AU - Jipeng XU
AU - Jie ZHENG
AU - Hai WANG
AU - Quanli GAO
PY - 2022
DO - 10.1587/transinf.2021EDP7185
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - With the popularity and development of Location-Based Services (LBS), location privacy-preservation has become a hot research topic in recent years, especially research on k-anonymity. Although previous studies have done a lot of work on anonymity-based privacy protection, there are still several challenges far from being perfectly solved, such as the negative impact on the security of anonymity by the semantic information, which from anonymous locations and query content. To address these semantic challenges, we propose a dual privacy preservation scheme based on the architecture of multi-anonymizers in this paper. Different from existing approaches, our method enhanced location privacy by integrating location anonymity and the encrypted query. First, the query encryption method that combines improved shamir mechanism and multi-anonymizers is proposed to enhance query safety. Second, we design an anonymity method that enhances semantic location privacy through anonymous locations that satisfy personal semantic diversity and replace sensitive semantic locations. Finally, the experiment on the real dataset shows that our algorithms provide much better privacy and use than previous solutions.
ER -
English
