A Dual Privacy Preserving Scheme in Continuous Location-Based Services
Abstract
With the development of wireless communication and positioning technology, location-based services (LBSs) have been gaining tremendous popularity, due to its ability to greatly facilitate the people's daily lives. Meanwhile, it also entails the risk of location privacy disclosure. To address this issue, general solutions introduce a single trusted anonymizer between the users and the location service provider (LSP). However, a single anonymizer offers limited privacy guarantees and incurs high communication overhead in continuous LBSs. Once the anonymizer is compromised, it may put the user information in jeopardy. In this paper, we propose a dual privacy preserving (DPP) scheme in continuous LBSs to protect the users' trajectory and query privacy. Our scheme introduces multiple anonymizers between the users and LSP, and combines with Shamir threshold mechanism, dynamic pseudonym mechanism, and K-anonymity technology to improve the users' trajectory and content privacy in continuous LBSs. An anonymizer alone cannot get the users' trajectory and query contents, and it thus can be semi-trusted. Our scheme can enhance the users' privacy and effectively solve the single point of failure in single anonymizer structure. At the same time, the query authentication can guarantee the correctness of the query results. The analysis and simulation results demonstrate that the proposed scheme has the ability to protect users' trajectory and content privacy effectively, and to reduce the computation and communication overhead of the single anonymizer.
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Semantic obfuscation algorithms are adopted to counter context-aware inference attacks.
“Hence, more robust protection methods like semantic obfuscation algorithms are adopted to counter these attacks [27].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [27]
Cites
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
Gedik, B., & Liu, L. (2007) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2007.1062 · Cited by 842 · see citing works
PrivacyProtector: Privacy-Protected Patient Data Collection in IoT-Based Healthcare Systems
Luo, E., Bhuiyan, M. Z. A., Wang, G., Rahman, M. A., Wu, J., & Atiquzzaman, M. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700364 · Cited by 266 · see citing works
Self-citationAn Obfuscation-Based Approach for Protecting Location Privacy
Ardagna, C. A., Cremonini, M., di Vimercati, S. D. C., et al. (2009) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2009.25 · Cited by 247 · see citing works
Authentic Third-Party Data Publication
Dévanbu, P., Gertz, M., Martel, C. U., et al. (2002) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/0-306-47008-x_9 · Cited by 227 · see citing works
Authentic data publication over the Internet1
Dévanbu, P., Gertz, M., Martel, C. U., et al. (2003) · Journal of Computer Security · doi:10.3233/jcs-2003-11302 · Cited by 175 · see citing works
Hiding in the Mobile Crowd: LocationPrivacy through Collaboration
Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E. M., & Hubaux, J. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.57 · Cited by 170 · see citing works
Collaborative trajectory privacy preserving scheme in location-based services
Peng, T., Liu, Q., Meng, D., & Wang, G. (2016) · Information Sciences · doi:10.1016/j.ins.2016.08.010 · Cited by 170 · see citing works
Self-citationEfficient algorithms for K-anonymous location privacy in participatory sensing
Vu, K., Zheng, R., & Gao, J. (2012) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2012.6195629 · Cited by 167 · see citing works
Karati, A., Islam, S. H., Biswas, G. P., Bhuiyan, M. Z. A., Vijayakumar, P., & Karuppiah, M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2741580 · Cited by 166 · see citing works
Self-citationPreserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks
Liu, Q., Wang, G., Li, F., Yang, S., & Wu, J. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2615020 · Cited by 147 · see citing works
Self-citationTrPF: A Trajectory Privacy-Preserving Framework for Participatory Sensing
Gao, S., Ma, J., Shi, W., Zhan, G., & Sun, C. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2252618 · Cited by 141 · see citing works
Protection of query privacy for continuous location based services
Pingley, A., Zhang, N., Fu, X., et al. (2011) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2011.5934968 · Cited by 139 · see citing works
A Novel Time-Obfuscated Algorithm for Trajectory Privacy Protection
Hwang, R., Hsueh, Y.-L., & Chung, H.-W. (2014) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2013.55 · Cited by 125 · see citing works
User-Defined Privacy Grid System for Continuous Location-Based Services
Schlegel, R., Chow, C.-Y., Huang, Q., & Wong, D. S. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2388488 · Cited by 108 · see citing works
Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services
Wang, T., Zeng, J., Bhuiyan, M. Z. A., Tian, H., Cai, Y., Chen, Y., & Zhong, B. (2017) · IEEE Access · doi:10.1109/access.2017.2698078 · Cited by 97 · see citing works
Self-citationEnhancing privacy through uniform grid and caching in location-based services
Zhang, S., Choo, K. R., Liu, Q., & Wang, G. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.06.022 · Cited by 96 · see citing works
Self-citationEnhanced Location Privacy Preserving Scheme in Location-Based Services
Peng, T., Liu, Q., & Wang, G. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2354235 · Cited by 93 · see citing works
Self-citationPrivacy-area aware dummy generation algorithms for Location-Based Services
Niu, B., Zhang, Z., Li, X., et al. (2014) · International Conference on Communications · doi:10.1109/icc.2014.6883443 · Cited by 87 · see citing works
Achieving reliable and secure services in cloud computing environments
Liu, Q., Wang, G., Liu, X., et al. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.10.005 · Cited by 82 · see citing works
Self-citationBrinkhoff, T. (2003) · Cited by 77 · see citing works
A trajectory privacy-preserving scheme based on query exchange in mobile social networks
Zhang, S., Wang, G., Liu, Q., & Abawajy, J. (2017) · Soft Computing · doi:10.1007/s00500-017-2676-6 · Cited by 54 · see citing works
Self-citationEfficient and Privacy-Preserving Polygons Spatial Query Framework for Location-Based Services
Zhu, H., Liu, F., & Li, H. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2553083 · Cited by 53 · see citing works
Lattice-Based Threshold Changeability for Standard Shamir Secret-Sharing Schemes
Steinfeld, R., Pieprzyk, J., & Wang, H. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.899541 · Cited by 52 · see citing works
EPLQ: Efficient Privacy-Preserving Location-Based Query Over Outsourced Encrypted Data
Li, L., Lu, R., & Huang, C. (2015) · IEEE Internet of Things Journal · doi:10.1109/jiot.2015.2469605 · Cited by 52 · see citing works
Privacy-Preserving Location Sharing Services for Social Networks
Schlegel, R., Chow, C.-Y., Huang, Q., et al. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2514338 · Cited by 49 · see citing works
Anonymizing popularity in online social networks with full utility
Zhang, S., Liu, Q., & Lin, Y. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.05.007 · Cited by 41 · see citing works
Self-citationPreserving the Location Privacy of Secondary Users in Cooperative Spectrum Sensing
Grissa, M., Yavuz, A. A., & Hamdaoui, B. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2622000 · Cited by 39 · see citing works
Liao, D., Sun, G., Li, H., Yu, H., & Chang, V. I. C. (2017) · Cluster Computing · doi:10.1007/s10586-017-0986-1 · Cited by 34 · see citing works
Privacy Enhanced Location Sharing for Mobile Online Social Networks
Son, J., Kim, D., Bhuiyan, M. Z. A., Tashakkori, R., Seo, J. T., & Lee, D. H. (2018) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2842788 · Cited by 22 · see citing works
Self-citationProtecting User Trajectory in Location-Based Services
Liao, D., Li, Y., Sun, G., et al. (2015) · Globecom · doi:10.1109/glocom.2015.7417512 · Cited by 14 · see citing works
A Parity-Based Data Outsourcing Model for Query Authentication and Correction
Tian, S., Cai, Y., & Hu, Z. (2016) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2016.78 · Cited by 4 · see citing works
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works
Zhang, S., Li, X., Tan, Z., Peng, T., & Wang, G. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.053 · Cited by 163
Self-citation
- Authors
- Zhang, S., Wang, G., Bhuiyan, M. Z. A., & Liu, Q.
- Affiliations
- Hunan University of Science and Technology (CN)
- Guangzhou University (CN)
- Hunan University (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2018-10
- Volume
- 5
- Issue
- 5
- Pages
- 4191-4200
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2807091904
- Cited by
- 159
- References
- 31
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 18 · 2023: 14 · 2022: 20 · 2021: 24 · 2020: 34 · 2019: 27 · 2018: 15
- FWCI
- 14.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- National Natural Science Foundation of China (61632009, 61472451, 61402161, 61772194)
- Natural Science Foundation of Guangdong Province (2017A030308006)
- High Level Talents Program of Higher Education in Guangdong Province (2016ZJ01)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- location privacy, location-based services, trajectory privacy, query privacy, k-anonymity, query authentication, single point of failure, communication overhead
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