[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Constructing dummy query sequences to protect location privacy and query privacy in location-based services

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://en.wikipedia.org/wiki/Jaccard_index

  2. https://en.wikipedia.org/wiki/Cosine_similarity

  3. http://snap.stanford.edu/data/loc-gowalla.html

  4. http://map.baidu.com

References

  1. Peng, T., Liu, Q., Wang, G.: Enhanced location privacy preserving scheme in location-based services. IEEE Syst. J. 11(1), 219–230 (2017)

    Article  Google Scholar 

  2. Zeng, S., Mu, Y., He, M., et al.: New approach for privacy-aware location-based service communications. Wirel. Pers. Commun. 11(2), 1057–1073 (2018)

    Article  Google Scholar 

  3. Li, Z., Pei, Q., Markwood, I., et al.: Location privacy violation via GPS-agnostic smart phone car tracking. IEEE Trans. Veh. Technol. 67(6), 5042–5053 (2018)

    Google Scholar 

  4. Ghaffari, M., Ghadiri, N., Manshaei, M.H., et al.: P4QS: A peer to peer privacy preserving query service for location-based mobile applications. IEEE Trans. Veh. Technol. 66(10), 9458–9469 (2017)

    Article  Google Scholar 

  5. Kalaiarasy, C., Sreenath, N., Amuthan, A.: Location privacy preservation in VANET using mix zones - a survey. Proc ICCCI , 1–5 (2019)

  6. Ding, X., Yang, W., Choo, R., et al.: Privacy-preserving similarity joins using MapReduce. Inf. Sci. 493, 20–33 (2019)

    Article  Google Scholar 

  7. Dewri, R., Thurimella, R.: Mobile local search with noisy locations. Pervasive Mob Comput 32, 78–92 (2016)

    Article  Google Scholar 

  8. Zhang, L., Li, J., Yang, S., et al.: Privacy preserving in cloud environment for obstructed shortest path query. Wirel. Pers. Commun. 96(2), 2305–2322 (2017)

    Article  Google Scholar 

  9. Niu, B., Li, Q., Zhu, Q., et al.: Achieving k-anonymity in privacy-aware location-based services (2014)

  10. Wu, Z., Li, G., Liu, Q., Xu, G., Chen, E.: Covering the sensitive subjects to protect personal privacy in personalized pecommendation. IEEE Trans. Serv. Comput. 11(3), 493–506 (2018)

    Article  Google Scholar 

  11. Li, F., Zhang, C., Niu, B., et al.: Efficient scheme for user’s trajectory privacy. Chin. J. Commun. 36(12), 114–123 (2015)

    Google Scholar 

  12. Ravi, N., Krishna, C., Koren, I.: Enhancing vehicular anonymity in ITS: a new scheme for mix zones and their placement. IEEE Trans. Veh. Technol. 68 (11), 10372–10381 (2019)

    Article  Google Scholar 

  13. Palanisamy, B., Liu, L., Lee, K., et al.: Anonymizing continuous queries with delay-tolerant mix-zones over road networks. Distrib Parallel Databases 32(1), 91–118 (2014)

    Article  Google Scholar 

  14. Memon, I., Ali, Q., Zubedi, A., et al.: DPMM: Dynamic Pseudonym-based multiple mix-zones generation for mobile traveler. Multimed Tools Appl, 1–30 (2016)

  15. Zhao, D., Jin, Y., Zhang, K., et al.: EPLA: efficient personal location anonymity. GeoInformatica 22(1), 29–47 (2018)

    Article  Google Scholar 

  16. Gedik, B., Liu, L.: Protecting location privacy with personalized k-anonymity: Architecture and algorithms. IEEE Trans. Mob. Comput. 7(1), 1–18 (2008)

    Article  Google Scholar 

  17. Xue, D., Wu, L., Li, H., et al.: A novel destination prediction attack and corresponding location privacy protection method in geo-social networks. Int. J. Distrib. Sens. Netw. 13(1), 1–16 (2017)

    Article  Google Scholar 

  18. Soma, S., Hashem, T., Cheema, M., et al.: Trip planning queries with location privacy in spatial databases. World Wide Web 20, 205–236 (2017)

    Article  Google Scholar 

  19. Agir, B., Papaioannou, T., Narendula, R., et al.: User-side adaptive protection of location privacy in participatory sensing. GeoInformatica 18 (1), 165–191 (2014)

    Article  Google Scholar 

  20. Dewri, R., Thurimella, R.: Exploiting service similarity for privacy in location-based search queries. IEEE Trans. Parallel Distrib. Syst. 25(2), 374–383 (2014)

    Article  Google Scholar 

  21. Andrs, M.E., Bordenabe, N.E., Chatzikokolakis, K., et al.: Geo-indistinguishability: Differential privacy for location-based systems. Proc. CCS, 901–914 (2013)

  22. Mendes, R., Cunha, M., Vilela, J.: Impact of frequency of location reports on the privacy level of geo-indistinguishability. Proc. Priv. Enhanc. Technol. 2, 379–396 (2020)

    Google Scholar 

  23. Dong, X., Zhang, T., Lu, D., et al.: Preserving geo-indistinguishability of the primary user in dynamic spectrum sharing. IEEE Trans. Veh. Technol. 68(9), 8881–8892 (2019)

    Article  Google Scholar 

  24. Lai, J., Mu, Y., Guo, F., et al.: Privacy-enhanced attribute-based private information retrieval. Inf. Sci. 454, 275–291 (2018)

    Article  MathSciNet  Google Scholar 

  25. Mei, Z., Zhu, H., Cui, Z., et al.: Executing multi-dimensional range query efficiently and flexibly over outsourced ciphertexts in the cloud. Inf. Sci. 432, 79–96 (2018)

    Article  Google Scholar 

  26. Zhang, L., Tang, S., Chen, J., et al.: Two-factor remote authentication protocol with user anonymity based on elliptic curve cryptography. Wirel. Pers. Commun. 81(1), 53–75 (2015)

    Article  Google Scholar 

  27. Ding, X., Liu, P., Jin, H.: Privacy-preserving multi-keyword top-k similarity search over encrypted Data. IEEE Trans. Depend. Secure Comput. 16(2), 344–357 (2019)

    Article  Google Scholar 

  28. Khoshgozaran, A., Shirani-Mehr, H., Shahabi, C.: Blind evaluation of location based queries using space transformation to preserve location privacy. GeoInformatica 17(4), 599–634 (2013)

    Article  Google Scholar 

  29. Meng, W., Lee, B., Xing, X., et al.: Trackmeornot: Enabling flexible control on web tracking. Proc. of WWW, 99–109 (2016)

  30. Wu, Z., Shi, J., Lu, C., et al.: Constructing plausible innocuous pseudo queries to protect user query intention. Inf. Sci. 325, 215–226 (2015)

    Article  Google Scholar 

  31. Wu, Z., Li, R., Xie, J., et al.: A user sensitive subject protection approach for book search service. J. Assoc. Inf. Sci. Technol. 71(2), 183–195 (2020)

    Article  Google Scholar 

  32. Leskovec, J., Sosic, R.: SNAP: A general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. 8(1), 1 (2016)

    Article  Google Scholar 

  33. Haus, M., Waqas, M., Ding, A., et al.: Security and privacy in device-to-device (D2D) communication: a review. IEEE Commun. Surv. Tutor. 19(2), 1054–1079 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China

    Zongda Wu & Shigen Shen

  2. School of Computer Science, China University of Geosciences, Wuhan, China

    Guiling Li

  3. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China

    Guiling Li

  4. Oujiang College, Wenzhou University, Wenzhou, Zhejiang, China

    Xinze Lian

  5. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  6. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

    Guandong Xu

Authors
  1. Zongda Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guiling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shigen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinze Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Enhong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guandong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinze Lian.

Additional information

Publisher’s note

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

The work is supported by the Zhejiang Provincial Natural Science Foundation of China (Nos. LZ18F020001 and LY19F020018), the National Natural Science Foundation of China (Nos. 61762055, 61702468 and 61962029), the National Social Science Foundation of China (No. 19BTQ056) and Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (No. KLIGIP-2018B03).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z., Li, G., Shen, S. et al. Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web 24, 25–49 (2021). https://doi.org/10.1007/s11280-020-00830-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-020-00830-x

Keywords

Search

Navigation