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pFind: Privacy-preserving lost object finding in vehicular crowdsensing

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Abstract

Web 3.0 makes crowdsensing services more popular, because of its decentralisation and interoperability. Lost Object Finding (LOF) in vehicular crowdsensing is an emerging paradigm in which vehicles act as detectors to find lost objects for their owners. To enjoy LOF services, object owners need to submit the tag ID of his lost object, and then detectors need to update their detecting results together with their locations. But the identity and location information are usually sensitive, which can be used to infer the locations of lost objects, or track participant detectors. This raises serious privacy concerns. In this paper, we study the privacy leakages associated with object finding, and propose a privacy-preserving scheme, named pFind, for locating lost objects. This scheme allows owners to retrieve the locations of their lost objects and provides strong privacy protection for the object owners, lost objects, and detectors. In pFind, we design an oblivious object detection protocol by using RBS cryptosystem, which simultaneously provides confidentiality, authentication and integrity for lost objects detection. Meanwhile, we propose a private location retrieval protocol to compute the approximate location of a lost object over encrypted data. We further propose two optimizations for pFind to enhance functionality and performance. Theoretical analysis and experimental evaluations show that pFind is secure, accurate and efficient.

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Notes

  1. www.bungeetags.com

  2. www.rockettags.com

  3. www.thetileapp.com

  4. www.perdidoonline.com

  5. www.wetaginc.com

  6. www.thetrackr.com

  7. www.cs.uic.edu/~sma/ridesharing

  8. https://acsc.cs.utexas.edu/libpaillier/

  9. https://github.com/gdanezis/petlib

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Funding

This work was partially supported by National Natural Science Foundation of China (Grant No. 62172123, 62302122) and Natural Science Foundation of Heilongjiang Province of China (Grant No. YQ2021F007).

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Authors and Affiliations

  1. School of Cyberspace Science, Harbin Institute of Technology, Harbin, China

    Yinggang Sun, Haining Yu, Xiang Li, Yizheng Yang & Xiangzhan Yu

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  1. Yinggang Sun
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  3. Xiang Li
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  4. Yizheng Yang
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Contributions

Yinggang Sun participated equally in study design, algorithm implementation, and drafting of the manuscript. Xiang Li and Yizheng Yang prepared figures.Xiangzhan Yu proofread the manuscript. Haining Yu was the corresponding author, supervised the study, and helped revise the manuscript. All authors read and approved the final manuscript.

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Correspondence to Haining Yu.

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This article belongs to the Topical Collection: Special Issue on Data Security Governance Technologies for Web 3.0

Guest Editors: Hui Lu, Mikko Valkama, Yanhua LI, Shen Su, Qian Zhou and Hua Wang

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Sun, Y., Yu, H., Li, X. et al. pFind: Privacy-preserving lost object finding in vehicular crowdsensing. World Wide Web 27, 64 (2024). https://doi.org/10.1007/s11280-024-01300-4

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