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A novel collaborative privacy protection scheme based on verifiable secret sharing and trust mechanism

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Abstract

In recent years, ensuring the privacy of location-based services (LBSs) has become a central concern. While various privacy protection strategies have been proposed, user collaboration remains a standard and widely-used solution to address service bottlenecks and attack vulnerabilities. Although it is widely used, challenges still remain. For collaboration to succeed, users must trust one another and be willing to cooperate, often forming anonymous groups. However, curious collaborators may attempt to learn other users' private information, or they may collude with service providers to extract location data. To address these issues, this paper proposes a Privacy Protection Scheme based on Verifiable Secret Sharing and Trust mechanism (VSS-TPPS). In this scheme, the requester encrypts and splits the main secret using a verifiable secret sharing algorithm, while providing a coefficient commitment to verify the sub-secret data, making it difficult for collaborative users to infer any information about the requester. Additionally, if fewer than t users collude, it becomes extremely challenging to form a complete query. By combining verifiable secret sharing with a trust mechanism, the scheme introduces competitive incentives, rewarding those cooperative users who submit partitioned information first. The simulation experiments verified the effectiveness of the proposed scheme in countering collusion attacks and inference attacks. Compared with the SCPPS, GCS, Tr-privacy, and Ik-anonymity schemes, the VSS-TPPS scheme improved average efficiency by approximately 23.64%, 99.80%, 96.26%, and 94.10%, respectively. The VSS-TPPS scheme not only enhances privacy protection but also significantly improves efficiency, demonstrating its effectiveness in user collaboration privacy protection.

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Data availability

The practical Geolife dataset in this paper is a GPS trajectory dataset from the Microsoft Research Project, which includes trajectory data from 182 users collected from April 2007 to August 2012, generating 17,620 trajectory messages

References

  1. Kong X, Wu Y, Wang H et al (2022) Edge computing for internet of everything: A survey. IEEE Internet Things J 9(23):23472–23485

    Article  Google Scholar 

  2. Jiang H, Li J, Zhao P et al (2021) Location privacy-preserving mechanisms in location-based services: a comprehensive survey. ACM Comput Surv (CSUR) 54(1):1–36. https://doi.org/10.1145/3423165

    Article  Google Scholar 

  3. Yang H, Vijayakumar P, Shen J et al (2022) A location-based privacy-preserving oblivious sharing scheme for indoor navigation. Futur Gener Comput Syst 137:42–52. https://doi.org/10.1016/j.future.2022.06.016

    Article  Google Scholar 

  4. Yang Z, Wang R, Wang H et al (2022) Cloud edge-client collaborative trajectory privacy protection system and technology. IEEE Netw 36(4):190–196. https://doi.org/10.1109/MNET.012.2000384

    Article  Google Scholar 

  5. Kaur J, Agrawal A, Khan RA (2022) Encryfuscation: a model for preserving data and location privacy in fog based IoT scenario. J King Saud Univ Comput Inf Sci 34(9):6808–6817. https://doi.org/10.1016/j.jksuci.2022.03.003

    Article  Google Scholar 

  6. Wu Y, Zhang C, Zhu L (2023) Privacy-preserving and traceable blockchain-based charging payment scheme for electric vehicles. IEEE Internet Things J 10(24):21254–21265

    Article  Google Scholar 

  7. Nisha N, Natgunanathan I, Xiang Y (2022) An enhanced location scattering based privacy protection scheme. IEEE Access 10:21250–21263

    Article  Google Scholar 

  8. Yang D, Ye B, Zhang W et al (2021) (2021) KLPPS: a k-anonymous location privacy protection scheme via dummies and stackelberg game. Secur Commun Netw 1:9635411. https://doi.org/10.1155/2021/9635411

    Article  Google Scholar 

  9. Guo P, Ye B, Chen Y et al (2022) A differential privacy protection protocol based on location entropy. Tsinghua Sci Technol 28(3):452–463

    Article  Google Scholar 

  10. Zhang L, Liu D, Chen M et al (2021) A user collaboration privacy protection scheme with threshold scheme and smart contract. Inf Sci 560:183–201

    Article  MathSciNet  Google Scholar 

  11. Nisha N, Natgunanathan I, Gao S et al (2022) A novel privacy protection scheme for location-based services using collaborative caching. Comput Netw 213:109107. https://doi.org/10.1016/j.comnet.2022.109107

    Article  Google Scholar 

  12. Guo L, Zhu Y, Yang H et al (2022) A k-nearest neighbor query method based on trust and location privacy protection. Concurr Comput Pract Exp 34(16):e5766. https://doi.org/10.1002/cpe.5766

    Article  Google Scholar 

  13. Wang J, Liu J, Chen J et al (2023) Credible nodes selection in mobile crowdsensing based on GAN. Appl Intell 53(19):22715–22727. https://doi.org/10.1007/s10489-023-04815-x

    Article  Google Scholar 

  14. Jorquera Valero JM, Sánchez PM, Gil Pérez M et al (2023) Cutting-edge assets for trust in 5G and beyond: requirements, state of the art, trends, and challenges. ACM Comput Surv 55(11):1–36. https://doi.org/10.1145/3572717

    Article  Google Scholar 

  15. Teng F, Du C, Shen M et al (2022) A dynamic large-scale multiple attribute group decision-making method with probabilistic linguistic term sets based on trust relationship and opinion correlation. Inf Sci 612:257–295

    Article  Google Scholar 

  16. Goodson DJ, van Riper CJ, Andrade R et al (2022) Perceived inclusivity and trust in protected area management decisions among stakeholders in Alaska. People Nat 4(3):758–772. https://doi.org/10.1002/pan3.10312

    Article  Google Scholar 

  17. Jones RL, Guha-Sapir D, Tubeuf S (2022) Human and economic impacts of natural disasters: can we trust the global data ? Sci Data 9(1):572. https://doi.org/10.1038/s41597-022-01667-x

    Article  Google Scholar 

  18. Lee SW, Hussain S, Issa GF et al (2021) Multi-dimensional trust quantification by artificial agents through evidential fuzzy multi-criteria decision making. IEEE Access 9:159399–159412

    Article  Google Scholar 

  19. Zulfiqar M, Kamran M, Rasheed MB (2022) A blockchain-enabled trust aware energy trading framework using games theory and multi-agent system in smat grid. Energy 255:124450. https://doi.org/10.1016/j.energy.2022.124450

    Article  Google Scholar 

  20. Mora L, Kummitha RKR, Esposito G (2021) Not everything is as it seems: Digital technology affordance, pandemic control, and the mediating role of sociomaterial arrangements. Gov Inf Q 38(4):101599

    Article  Google Scholar 

  21. Sun P (2020) Research on cloud computing service based on trust access control. Int J Eng Bus Manag 12:1847979019897444. https://doi.org/10.1177/1847979019897444

    Article  Google Scholar 

  22. Wang Y, Wen J, Zhou W (2019) A trust-based evaluation model for data privacy protection in cloud computing. Int J High Perform Comput Netw 14(2):147–156. https://doi.org/10.1504/IJHPCN.2019.101251

    Article  Google Scholar 

  23. Rahman MM, Abdullah NA (2023) A trustworthiness-aware spatial task allocation using a fuzzy-based trust and reputation system approach. Expert Syst Appl 211:118592

    Article  Google Scholar 

  24. Ogundoyin SO, Kamil IA (2021) A trust management system for fog computing services. Internet Things 14:100382. https://doi.org/10.1016/j.iot.2021.100382

    Article  Google Scholar 

  25. Shehada D, Gawanmeh A, Yeun CY et al (2022) Fog-based distributed trust and reputation management system for internet of things. J King Saud Univ Comput Inf Sci 34(10):8637–8646

    Google Scholar 

  26. Li X, Miao M, Liu H et al (2017) An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism. Soft Comput 21:3907–3917. https://doi.org/10.1007/s00500-016-2040-2

    Article  Google Scholar 

  27. Xu L, Jiang C, He N et al (2018) Trust-based collaborative privacy management in online social networks. IEEE Trans Inf Forensics Secur 14(1):48–60. https://doi.org/10.1109/TIFS.2018.2840488

    Article  Google Scholar 

  28. Gao L, Li L, Chen Y et al (2022) FGFL: a blockchain-based fair incentive governor for federated learning. J Parallel Distrib Comput 163:283–299

    Article  Google Scholar 

  29. Hu J, Yuan J (2022) A location privacy protection method based on location semantics over road networks. In: International conference on computer application and information security (ICCAIS 2021), vol 12260. SPIE, pp 417–424

  30. Shen Z, Lu S, Huang H et al (2020) An approach based on customized robust cloaked region for geographic location information privacy protection. Mob Inf Syst 2020(1):3903681. https://doi.org/10.1155/2020/3903681

    Article  Google Scholar 

  31. Yadav VK, Verma S, Venkatesan S (2022) Efficient and privacy-preserving location-based services over the cloud. Clust Comput 25(5):3175–3192. https://doi.org/10.1007/s10586-021-03533-8

    Article  Google Scholar 

  32. Sandor VKA, Lin Y, Li X et al (2019) Efficient decentralized multi-authority attribute based encryption for mobile cloud data storage. J Netw Comput Appl 129:25–36

    Article  Google Scholar 

  33. Liu Z, Liu Q, Wei J et al (2022) Location privacy-preserving query scheme based on the Moore curve and multi-user cache. Information 13(9):417. https://doi.org/10.3390/info13090417

    Article  Google Scholar 

  34. Yadav VK, Andola N, Verma S et al (2022) Anonymous and linkable location-based services. IEEE Trans Veh Technol 71(9):9397–9409. https://doi.org/10.1109/TVT.2022.3180412

    Article  Google Scholar 

  35. Wang JJ, Han YL, Yang XY et al (2019) (2019) A new group location privacy-preserving method based on distributed architecture in LBS. Secur Commun Netw 1:2414687. https://doi.org/10.1155/2019/2414687

    Article  Google Scholar 

  36. Yadav VK, Verma S, Venkatesan S (2021) Linkable privacy-preserving scheme for location-based services. IEEE Trans Intell Transp Syst 23(7):7998–8012. https://doi.org/10.1109/TITS.2021.3074974

    Article  Google Scholar 

  37. Zhao F, Peng C, Xu D et al (2023) Attribute-based multi-user collaborative searchable encryption in COVID-19. Comput Commun 205:118–126

    Article  Google Scholar 

  38. Yadav VK, Andola N, Verma S et al (2021) P2LBS: privacy provisioning in location-based services. IEEE Trans Serv Comput 16(1):466–477. https://doi.org/10.1109/TSC.2021.3123428

    Article  Google Scholar 

  39. Feldman P (1987) A practical scheme for non-interactive verifiable secret sharing. In: 28th annual symposium on foundations of computer science (sfcs 1987), pp 427–438. https://doi.org/10.1109/SFCS.1987.4

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Funding

The Natural Science Fund of Heilongjiang Province (LH2021F054), the Basic Scientific Research Operating Expenses of Heilongjiang Provincial Universities and Colleges for Excellent Innovation Team (2022-KYYWF-0654), the National Fund cultivation project of Jiamusi University (JMSUGPZR2022-014), the Excellent Discipline Team Project of Jiamusi University (JDXKTDG2019008), the Basic Research Support Program for Outstanding Young Teachers in Undergraduate Universities of Heilongjiang Province (YQJH2024239), the Foreign Expert Project of Heilongjiang Province (G2024020), Heilongjiang Province Philosophy and Social Science Research Planning Project (JMSUBZ2024-07).

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Author notes

  1. Lei Zhang, Mingzeng Cao, Jing Li have contributed equally to this work.

Authors and Affiliations

  1. School of Information and Electronics Technology, Jiamusi University, Jiamusi, 154007, Heilongjiang, China

    Lei Zhang, Mingzeng Cao, Jing Li, Chenglin Zhang & Lili He

  2. The Heilongjiang Provincial Key Laboratory of Autonomous Intelligence and Information Processing, School of Information Science and Electronic Technology, Jiamusi University, Jiamusi, 154007, Heilongjiang, China

    Lei Zhang, Mingzeng Cao, Jing Li, Chenglin Zhang & Lili He

Authors
  1. Lei Zhang
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Contributions

Lei Zhang: Conceptualization, Methodology, Writing—original draft. Jing Li: Funding acquisition, Formal analysis. Mingzeng Cao: Writing—review & editing, Resources, Data curation. Chenglin Zhang: Project administration, Investigation. Lili He: Software.

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Correspondence to Jing Li.

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Zhang, L., Cao, M., Li, J. et al. A novel collaborative privacy protection scheme based on verifiable secret sharing and trust mechanism. Computing 107, 23 (2025). https://doi.org/10.1007/s00607-024-01361-3

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