The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Com a popularidade e o desenvolvimento dos Serviços Baseados em Localização (LBS), a preservação da privacidade da localização tornou-se um tema de pesquisa importante nos últimos anos, especialmente pesquisas sobre k-anonimato. Embora estudos anteriores tenham feito muito trabalho sobre a proteção da privacidade baseada no anonimato, ainda existem vários desafios longe de serem perfeitamente resolvidos, como o impacto negativo na segurança do anonimato pela informação semântica, proveniente de locais anónimos e de conteúdos de consulta. Para enfrentar esses desafios semânticos, propomos neste artigo um esquema duplo de preservação de privacidade baseado na arquitetura de multi-anonimizadores. Diferente das abordagens existentes, nosso método melhorou a privacidade da localização integrando o anonimato da localização e a consulta criptografada. Primeiro, o método de criptografia de consulta que combina mecanismo shamir aprimorado e multianonimizadores é proposto para aumentar a segurança da consulta. Em segundo lugar, projetamos um método de anonimato que aumenta a privacidade semântica da localização por meio de localizações anônimas que satisfazem a diversidade semântica pessoal e substituem localizações semânticas sensíveis. Finalmente, a experiência no conjunto de dados real mostra que os nossos algoritmos proporcionam uma privacidade e uma utilização muito melhores do que as soluções anteriores.
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
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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},}
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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 -