Skip to main content
SpringerLink
Log in

Privacy-Preserving Traffic Violation Image Filtering and Searching via Crowdsensing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

With the popularity of mobile terminal equipment and wireless sensing network, the applications of mobile crowdsensing-based traffic violation monitoring are increasingly widely used. However, the enormous amount of sensing data with complex types brings a critical challenge to the limited bandwidth and storage space. Meanwhile, there is a serious risk of the sensing data and query privacy leakage in multi-requester/multi-user scenarios. To address the above issues, we propose a traffic violation image filtering and searching scheme for multi-requester/multi-user mobile crowdsensing, which achieves image content and user query privacy preservation. Specifically, we firstly consider the multiple factors that impaired image quality, then give the grading metric to perform image filtering and obtain high-quality images. In query and searching processes, we achieve that unshared key multi-requester/multi-user image retrieval without any image content and query privacy leakage. Moreover, our proposed scheme supports the malicious users’ accountability based on the revealed private keys, which significantly improve the security and reliability. Finally, we conduct the privacy analysis, which satisfies the privacy-preserving and security requirements. Experiment results on real-world dataset show that our approach to image filtering and searching is practical and effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://www.openits.cn/openData4/748.jhtml

References

  1. Basudan S, Lin X, Sankaranarayanan K (2017) A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE Internet Things J 4(3):772–782

    Article  Google Scholar 

  2. Chen H, Guo B, Yu Z, Chen L, Ma X (2017) A generic framework for constraint-driven data selection in mobile crowd photographing. IEEE Internet Things J 4(1):284–296

    Google Scholar 

  3. Dong X, Zhang W, Shah M, Wang B, Yu N (2020) Watermarking-based secure plaintext image protocols for storage, show, deletion and retrieval in the cloud. IEEE Transactions on Services Computing

  4. Guo B, Chen H, Yu Z, Nan W, Xie X, Zhang D, Zhou X (2017) Taskme: toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. Int J Human-Comput Stud 102:14–26

    Article  Google Scholar 

  5. Guo B, Chen H, Yu Z, Xie X, Huangfu S, Zhang D (2014) Fliermeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans Mob Comput 14(10):2020–2033

    Article  Google Scholar 

  6. Guo B, Wang Z, Yu Z, Wang Y, Yen NY, Huang R, Zhou X (2015) Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput Surveys (CSUR) 48(1):7.1–7.31

    Article  Google Scholar 

  7. Jiang Q, Zhang N, Ni J, Ma J, Ma X, Choo KKR (2020) Unified biometric privacy preserving three-factor authentication and key agreement for cloud-assisted autonomous vehicles. IEEE Trans Veh Technol 69(9):9390–9401. https://doi.org/10.1109/TVT.2020.2971254

    Article  Google Scholar 

  8. Jiang Q, Zhang X, Zhang N, Tian Y, Ma X, Ma J (2021) Three-factor authentication protocol using physical unclonable function for iov. Comput Commun, 1–1

  9. Khan S, Zhu L, Yu X, Zhang Z, Rahim MA, Khan M, Du X, Guizani M (2020) Accountable credential management system for vehicular communication. Veh Commun 25(100):279

    Google Scholar 

  10. Li M, Zhu L, Lin X (2019) Privacy-preserving traffic monitoring with false report filtering via fog-assisted vehicular crowdsensing. IEEE Transactions on Services Computing

  11. Li Y, Ma J, Miao Y, Liu L, Liu X, Choo KKR (2020) Secure and verifiable multi-key image search in cloud-assisted edge computing. IEEE Transactions on Industrial Informatics

  12. Li Y, Ma J, Miao Y, Wang Y, Liu X, Choo KKR (2020) Similarity search for encrypted images in secure cloud computing. IEEE Transactions on Cloud Computing

  13. Lin Y, Tu Y, Dou Z, Chen L, Mao S (2020) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Transactions on Cognitive Communications and Networking

  14. Lin Y, Zhao H, Ma X, Tu Y, Wang M (2020) Adversarial attacks in modulation recognition with convolutional neural networks. IEEE Transactions on Reliability

  15. Liu Y, Yu JJQ, Kang J, Niyato D, Zhang S (2020) Privacy-preserving traffic flow prediction: a federated learning approach. IEEE Internet Things J 7(8):7751–7763. https://doi.org/10.1109/JIOT.2020.2991401

    Article  Google Scholar 

  16. Luo C, Ji J, Wang Q, Chen X, Li P (2020) Channel state information prediction for 5g wireless communications: A deep learning approach. IEEE Trans Netw Sci Eng 7(1):227–236

    Article  MathSciNet  Google Scholar 

  17. Ma M, Preum SM, Ahmed MY, Tärneberg W, Hendawi A, Stankovic JA (2019) Data sets, modeling, and decision making in smart cities: a survey. ACM Trans Cyber-Phys Syst 4(2):1–28

    Article  Google Scholar 

  18. Nabil M, Sherif A, Mahmoud M, Alsharif A, Abdallah M (2019) Efficient and privacy-preserving ridesharing organization for transferable and non-transferable services. IEEE Transactions on Dependable and Secure Computing

  19. Nakamura K, Nitta N, Babaguchi N (2018) Encryption-free framework of privacy-preserving image recognition for photo-based information services. IEEE Trans Inform Foren Secur 14(5):1264–1279

    Article  Google Scholar 

  20. Shamir A (1979) How to share a secret. Commun ACM 22(11):359,168–359,176

    Article  MathSciNet  MATH  Google Scholar 

  21. Shu J, Wang S, Jia X, Zhang W, Xie R, Huang H (2020) Efficient lane-level map building via vehicle-based crowdsourcing. IEEE Transactions on Intelligent Transportation Systems

  22. Shu J, Yang K, Jia X, Liu X, Wang C, Deng R (2018) Proxy-free privacy-preserving task matching with efficient revocation in crowdsourcing. IEEE Transactions on Dependable and Secure Computing

  23. Silva SM, Jung CR (2018) License plate detection and recognition in unconstrained scenarios. In: Proceedings of the European conference on computer vision (ECCV), pp 580–596

  24. Tian Y, Wang Z, Xiong J, Ma J (2020) A blockchain-based secure key management scheme with trustworthiness in dwsns. IEEE Trans Industr Inform 16(9):6193–6202

    Article  Google Scholar 

  25. Wang R, Kan Z, Cui Y, Wu D, Zhen Y (2021) Cooperative caching strategy with content request prediction in internet of vehicles. IEEE Internet of Things Journal, 1–1

  26. Wang X, Ma J, Liu X, Miao Y (2019) Search in my way: practical outsourced image retrieval framework supporting unshared key. In: IEEE INFOCOM 2019-IEEE conference on computer communications. IEEE, pp 2485–2493

  27. Weppner J, Lukowicz P (2013) Bluetooth based collaborative crowd density estimation with mobile phones. In: 2013 IEEE International conference on pervasive computing and communications (PerCom). IEEE, pp 193–200

  28. Wu Q, Li Y, Lin Y (2017) The application of nonlocal total variation in image denoising for mobile transmission. Multimed Tools Applic 76(16):17,179–17,191

    Article  Google Scholar 

  29. Wu Q, Li Y, Lin Y, Yang X (2014) The nonlocal sparse reconstruction algorithm by similarity measurement with shearlet feature vector. Math Probl Eng, 2014

  30. Wu Y, Wang Y, Cao G (2017) Photo crowdsourcing for area coverage in resource constrained environments. In: IEEE INFOCOM 2017-IEEE Conference on computer communications. IEEE, pp 1–9

  31. Wu Y, Wang Y, Hu W, Cao G (2015) Smartphoto: a resource-aware crowdsourcing approach for image sensing with smartphones. IEEE Trans Mob Comput 15(5):1249–1263

    Article  Google Scholar 

  32. Xiao B, Ou G, Tang H, Bi X, Li W (2019) Multi-focus image fusion by hessian matrix based decomposition. IEEE Trans Multimed 22(2):285–297

    Article  Google Scholar 

  33. Xiao B, Xu B, Bi X, Li W (2020) Global-feature encoding u-net (geu-net) for multi-focus image fusion. IEEE Trans Image Process 30:163–175

    Article  Google Scholar 

  34. Xiong J, Bi R, Zhao M, Guo J, Yang Q (2020) Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles. IEEE Wirel Commun 27(3):24–30

    Article  Google Scholar 

  35. Xiong J, Chen L, Bhuiyan A, Cao C, Wang M, Luo E, Liu X (2020) A secure data deletion scheme for iot devices through key derivation encryption and data analysis. Fut Gen Comput Syst (Early Access) 111:741–753

    Article  Google Scholar 

  36. Xiong J, Chen X, Yang Q, Chen L, Yao Z (2020) A task-oriented user selection incentive mechanism in edge-aided mobile crowdsensing. IEEE Trans Netw Sci Eng 7(4):2347–2360. https://doi.org/10.1109/TNSE.2019.2940958

    Article  Google Scholar 

  37. Xiong J, Ma R, Chen L, Tian Y, Li Q, Liu X, Yao Z (2020) A personalized privacy protection framework for mobile crowdsensing in iiot. IEEE Trans Industr Inform 16(6):4231–4241

    Article  Google Scholar 

  38. Xiong J, Zhang Y, Lin L, Shen J, Li X, Lin M (2017) ms-posw: a multi-server aided proof of shared ownership scheme for secure deduplication in cloud. In: Concurrency and computation practice and experience

  39. Xiong J, Zhao M, Bhuiyan MZA, Chen L, Tian Y (2021) An ai-enabled three-party game framework for guaranteed data privacy in mobile edge crowdsensing of iot. IEEE Trans Industr Inform 17(2):922–933

    Article  Google Scholar 

  40. Zhang C, Li W, Luo Y, Hu Y (2021) Ait: an ai-enabled trust management system for vehicular networks using blockchain technology. IEEE Internet Things J 8(5):3157–3169. https://doi.org/10.1109/JIOT.2020.3044296

    Article  Google Scholar 

  41. Zhang C, Zhu L, Ni J, Huang C, Shen X (2020) Verifiable and privacy-preserving traffic flow statistics for advanced traffic management systems. IEEE Trans Veh Technol 69(9):10,336–10,347

    Article  Google Scholar 

  42. Zhang K, Ni J, Yang K, Liang X, Ren J, Shen XS (2017) Security and privacy in smart city applications: challenges and solutions. IEEE Commun Mag 55(1):122–129

    Article  Google Scholar 

  43. Zhang L, Jung T, Liu C, Ding X, Li XY, Liu Y (2015) Pop: privacy-preserving outsourced photo sharing and searching for mobile devices. In: 2015 IEEE 35th International conference on distributed computing systems. IEEE, pp 308– 317

  44. Zhang L, Li XY, Liu K, Liu C, Ding X, Liu Y (2018) Cloak of invisibility: privacy-friendly photo capturing and sharing system. IEEE Trans Mob Comput 18(11):2488–2501

    Article  Google Scholar 

  45. Zhang L, Li Y, Xiao X, Li XY, Wang J, Zhou A, Li Q (2018) Crowdbuy: privacy-friendly image dataset purchasing via crowdsourcing. In: IEEE INFOCOM 2018-IEEE Conference on computer communications. IEEE, pp 2735–2743

  46. Zhang S, Yang G, Mu Y (2016) Linear encryption with keyword search. In: Australasian conference on information security and privacy. Springer, pp 187–203

  47. Zhang Y, Deng RH, Zheng D, Li J, Wu P, Cao J (2019) Efficient and robust certificateless signature for data crowdsensing in cloud-assisted industrial iot. IEEE Trans Industr Inform 15(9):5099–5108. https://doi.org/10.1109/TII.2019.2894108

    Article  Google Scholar 

  48. Zhao Y, Jiang H, Qin Y, Xie H, Wu Y, Liu S, Zhou Z, Xia J, Zhou F (2021) Preserving minority structures in graph sampling. IEEE Trans Vis Comput Graph 27(2):1–10

    Article  Google Scholar 

  49. Zhou T, Xiao B, Cai Z, Xu M (2019) A utility model for photo selection in mobile crowdsensing. IEEE Transactions on Mobile Computing

  50. Zhou T, Xiao B, Cai Z, Xu M, Liu X (2018) From uncertain photos to certain coverage: a novel photo selection approach to mobile crowdsensing. In: IEEE INFOCOM 2018-IEEE Conference on computer communications. IEEE, pp 1979–1987

  51. Zhu L, Li M, Zhang Z, Qin Z (2018) Asap: an anonymous smart-parking and payment scheme in vehicular networks. IEEE Trans Depend Sec Comput 17(4):703–715

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Yuanyuan Zhang

  2. Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, China

    Jinbo Xiong

  3. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China

    Jinbo Xiong

  4. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Ximeng Liu

Authors
  1. Yuanyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinbo Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ximeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinbo Xiong.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Xiong, J. & Liu, X. Privacy-Preserving Traffic Violation Image Filtering and Searching via Crowdsensing. Mobile Netw Appl 27, 2374–2390 (2022). https://doi.org/10.1007/s11036-021-01882-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01882-7

Keywords

Search

Navigation