Skip to main content
SpringerLink
Log in

Privacy Preserving Profile Matching in Mobile Social Networks: A Comprehensive Survey

  • Conference paper
  • First Online:
Security in Computing and Communications (SSCC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1208))

Included in the following conference series:

Abstract

The advancements in wireless communication technologies and smart mobile devices enabled the proliferation of Mobile Social Networks (MSN). MSN is more flexible and popular than Online Social Network (OSN). It provides a platform for intelligent device users to search on the Internet and connect to people in close proximity to obtain the required information. Connecting with other people in close proximity is the prominent feature of MSN. In social network connections are built based on common interest and location traces. To connect with more people who have similar interest profile matching is one of the essential steps. A new connection is built by comparing the profile of two strange users. During this process, private information of a user may get leaked. A major issue greatly raised and potentially vulnerable in MSN is user privacy preservation. During profile matching the ill-intended user may receive the private information of another user and misuse it. The issue of privacy in profile matching is focused by many researchers. This paper reviews the work done in the domain of privacy and security issues of profile matching and provides a comprehensive analysis on it.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 53 Incredible Facebook Statistics and Facts. https://www.brandwatch.com/blog/facebook-statistics/

  2. The Top 10 Most Popular Sites of 2019. https://www.lifewire.com/most-popular-sites-3483140

  3. Foursquare (2012). https://foursquare.com/

  4. Nissenbaum, H.: A contextual approach to privacy online. Daedalus 140(4), 32–48 (2011)

    Article  Google Scholar 

  5. Abbas, A., Khan, S.: A Review on the state-of-the-art privacy-preserving approaches in the e-Health clouds. IEEE J. Biomed. Health Inform. 18(4), 1431–1441 (2014)

    Article  Google Scholar 

  6. Li, M., Ruan, N., Qian, Q., Zhu, H., Liang, X., Yu, L.: SPFM: scalable and privacy-preserving friend matching in mobile cloud. IEEE Internet Things J. 4(2), 583–591 (2017)

    Article  Google Scholar 

  7. Luo, E., Guo, K., Tang, Y., Ying, X., Huang, W.: Hidden the true identity and dating characteristics based on quick private matching in mobile social networks. Future Gener. Comput. Syst. (2018). https://doi.org/10.1016/j.future.2018.04.088

  8. Li, M., Cao, N., Yu, S., Lou, W.: Findu: privacy preserving personal profile matching in mobile social networks. In: Proceedings of IEEE INFOCOM (2011)

    Google Scholar 

  9. Zhu, H., Du, S., Li, M., Gao, Z.: Fairness-aware and privacy-preserving friend matching protocol in mobile social networks. IEEE Trans. Emerg. Top. Comput. 1(1), 192–200 (2013)

    Article  Google Scholar 

  10. Cui, W., Du, C., Chen, J.: CP-ABE based privacy-preserving user profile matching in mobile social networks. PLoS ONE 11(6), e0157933 (2016)

    Article  Google Scholar 

  11. Shewale, K., Babar, S.D.: An efficient profile matching protocol using privacy preserving in mobile social network. Procedia Comput. Sci. 79, 922–931 (2016)

    Article  Google Scholar 

  12. Sommer, M., Lim, L., Li, D.: A differentially private matching scheme for pairing similar users of proximity-based social networking applications. In: Proceedings of the 51st Hawaii International Conference on System Sciences (2018)

    Google Scholar 

  13. Zhang, L., Li, X.-Y., Liu, K., Jung, T., Liu, Y.: Message in a sealed bottle: privacy-preserving friending in mobile social networks. IEEE Trans. Mob. Comput. 14(9), 1888–1902 (2015)

    Article  Google Scholar 

  14. Wang, Y., Hou, J., Xia, Y., Li, H.-Z.: Efficient privacy preserving matchmaking for mobile social networking. Concurr. Comput.: Pract. Exp. 27(12), 2924–2937 (2015)

    Article  Google Scholar 

  15. Abbas, F., Rajput, U., Oh, H.: PRISM: privacy-aware interest sharing and matching in mobile social networks. IEEE Access 4, 2594–2603 (2016)

    Article  Google Scholar 

  16. Niu, B., Zhu, X., Zhang, T., Chi, H., Li, H.: P-match: priority-aware friend discovery for proximity-based mobile social networks. In: Proceedings of the IEEE MASS, pp. 351–355 (2013)

    Google Scholar 

  17. Zhang, R., et al.: Privacy-preserving profile matching for proximity-based mobile social networking. IEEE J. Sel. Areas Commun. 31(9), 656–668 (2013)

    Article  Google Scholar 

  18. Luo, E., Liu, Q., Abawajy, J.H., Wang, G.: Privacy-preserving multi-hop profile-matching protocol for proximity mobile social networks. Future Gener. Comput. Syst. 68, 222–233 (2017)

    Article  Google Scholar 

  19. Gao, C.-Z., Cheng, Q., Li, X., Xia, S.-B.: Cloud-assisted privacy-preserving profile-matching scheme under multiple keys in mobile social network. Cluster Comput. 22(1), 1655–1663 (2019)

    Article  Google Scholar 

  20. Zhu, X., Liu, J., Jiang, S., Chen, Z., Li, H.: Efficient weight-based private matching for proximity-based mobile social networks. In: Proceedings of the IEEE ICC, pp. 4114–4119 (2014)

    Google Scholar 

  21. Li, R., et al.: Perturbation-based private profile matching in social networks. IEEE Access 5, 19720–19732 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pillai HOC College of Engineering and Technology (Affiliated to University of Mumbai), Khalapur, HOC Colony Road, Taluka, Rasayani, Maharashtra, India

    Rohini Bhosale & Madhumita Chatterjee

Authors
  1. Rohini Bhosale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madhumita Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rohini Bhosale .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Trivandrum, India

    Sabu M. Thampi

  2. University of Murcia, Espinardo, Murcia, Spain

    Gregorio Martinez Perez

  3. University of Queensland, Brisbane, QLD, Australia

    Ryan Ko

  4. Howard University, Washington, DC, USA

    Danda B. Rawat

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhosale, R., Chatterjee, M. (2020). Privacy Preserving Profile Matching in Mobile Social Networks: A Comprehensive Survey. In: Thampi, S., Martinez Perez, G., Ko, R., Rawat, D. (eds) Security in Computing and Communications. SSCC 2019. Communications in Computer and Information Science, vol 1208. Springer, Singapore. https://doi.org/10.1007/978-981-15-4825-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4825-3_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4824-6

  • Online ISBN: 978-981-15-4825-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation