Soft Computing

, Volume 22, Issue 8, pp 2517–2526 | Cite as

A privacy-preserving fuzzy interest matching protocol for friends finding in social networks

  • Xu An Wang
  • Fatos Xhafa
  • Xiaoshuang Luo
  • Shuaiwei Zhang
  • Yong Ding
Methodologies and Application

Abstract

Nowadays, it is very popular to make friends, share photographs, and exchange news throughout social networks. Social networks widely expand the area of people’s social connections and make communication much smoother than ever before. In a social network, there are many social groups established based on common interests among persons, such as learning group, family group, and reading group. People often describe their profiles when registering as a user in a social network. Then social networks can organize these users into groups of friends according to their profiles. However, an important issue must be considered, namely many users’ sensitive profiles could have been leaked out during this process. Therefore, it is reasonable to design a privacy-preserving friends-finding protocol in social network. Toward this goal, we design a fuzzy interest matching protocol based on private set intersection. Concretely, two candidate users can first organize their profiles into sets, then use Bloom filters to generate new data structures, and finally find the intersection sets to decide whether being friends or not in the social network. The protocol is shown to be secure in the malicious model and can be useful for practical purposes.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61272492, 61572521), the Natural Science Foundation of Shaanxi Province (2014JM8300), and Guangxi Key Laboratory of Cryptography and Information Security (No. GCIS201610).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Bloom B (1970) Space/time trade-offs in hash coding with allowable errors. Commun ACM 13(7):422–426CrossRefMATHGoogle Scholar
  2. Bose P, Guo H, Kranakis E, Maheshwari A, Morin P, Morrison J, Smid MHM, Tang Y (2008) On the false-positive rate of bloom filters. Inf Process Lett 108(4):210–213MathSciNetCrossRefMATHGoogle Scholar
  3. Camenisch J, Zaverucha GM (2009) Private intersection of certified sets. In: Dingledine R, Golle P (eds) FC 2009. LNCS, vol 5628. Springer, Berlin, pp 108–127Google Scholar
  4. Cheielewski L, Hoepman J (2008) Fuzzy private matching (extended abstract). In: Third international conference on IEEE availability, reliability and securityGoogle Scholar
  5. Chen C, Pai P, Hung W (2013) A new decision making process for selecting project leader based on social network and knowledge map. Int J Fuzzy Syst 15(1):36–46MathSciNetGoogle Scholar
  6. Cristina D, Elena A, Catalin L, Valentin C (2014) A solution for the management of multimedia sessions in hybrid clouds. Int J Space-Based Situated Comput 4(2):77–87CrossRefGoogle Scholar
  7. Dachman-Soled D, Malkin T, Raykova M, Yung M (2009) Efficient robust private set intersection. In: Abdalla M, Pointcheval D, Fouque PA, Vergnaud D (eds) ACNS 09. LNCS, vol 5536. Springer, Berlin, pp 125–142Google Scholar
  8. Dai W (2009) Crypto++ library: 5.6.0 benchmarks. http://www.cryptopp.com
  9. De Cristofaro E, Kim J, Tsudik G (2010) Linear-complexity private set intersection protocols secure in malicious model. In: Abe M (ed) ASIACRYPT 2010. LNCS, vol 6477. Springer, Berlin, pp 213–231 (2010)Google Scholar
  10. De Cristofaro E, Tsudik G (2010) Practical private set intersection protocols with linear complexity. In: Sion R (ed) FC 2010. LNCS, vol 6052. Springer, Berlin, pp 143–159Google Scholar
  11. Debnath SK, Dutta R (2015) Secure and efficient private set intersection cardinality using bloom filter. In: ISC 2015. LNCS, Springer, Berlin, pp 209–226Google Scholar
  12. Dong C, Chen L, Wen Z (2013) When private set intersection meets big data: an efficient and scalable protocol. In: Sadeghi AR, Gligor VD, Yung M (eds) ACM CCS 13. ACM Press, pp 789–800Google Scholar
  13. Freedman MJ, Nissim K, Pinkas B (2004) Efficient private matching and set intersection. In: Cachin C, Camenisch J (eds) EUROCRYPT 2004. LNCS, vol 3027. Springer, Berlin, pp 1–19Google Scholar
  14. Fu Z, Ren K, Shu J, Sun X, Huang F (2015) Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans Parallel Distrib Syst. doi: 10.1109/TPDS.2015.2506573
  15. Fu Z, Wu X, Guan C, Sun X, Ren K (2016) Towards efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans Inf Forensics Secur. doi: 10.1109/TIFS.2016.2596138
  16. Guo S, Xu H (2015) A secure delegation scheme of large polynomial computation in multi-party cloud. Int J Grid Util Comput 6(2):1–7Google Scholar
  17. Hazay C, Lindell Y (2008) Efficient protocols for set intersection and pattern matching with security against malicious and covert adversaries. In: Canetti R (ed) TCC 2008. LNCS, vol 4948. Springer, Berlin, pp 155–175Google Scholar
  18. Hazay C, Nissim K (2010) Efficient set operations in the presence of malicious adversaries. In: Nguyen PQ, Pointcheval D (eds) PKC 2010. LNCS, vol 6056. Springer, Berlin, pp 312–331Google Scholar
  19. Hu J, Hu Y, Bein H (2011) Constructing a corporate social responsibility fund using fuzzy multiple criteria decision making. Int J Fuzzy Syst 13(3):195–205Google Scholar
  20. Jarecki S, Liu X (2009) Efficient oblivious pseudorandom function with applications to adaptive OT and secure computation of set intersection. In: Reingold O (ed) TCC 2009. LNCS, vol 5444. Springer, Berlin, pp 577–594Google Scholar
  21. Jarecki S, Liu X (2010) Fast secure computation of set intersection. In: Garay JA, Prisco RD (eds) SCN 10. LNCS, vol 6280. Springer, Berlin, pp 418–435Google Scholar
  22. Kerschbaum F (2012) Outsourced private set intersection using homomorphic encryption. In: Youm HY, Won Y (eds) ASIACCS 12. ACM Press, pp 85–86Google Scholar
  23. Kissner L, Song DX (2005) Privacy-preserving set operations. In: Shoup V (ed) CRYPTO 2005. LNCS, vol 3621. Springer, Berlin, pp 241–257Google Scholar
  24. Liu X, Deng R, Ding W, Lu R, Qin B (2016) Privacy-preserving outsourced calculation of floating point numbers. IEEE Trans Inf Forensics Secur 11(11):2513–2527CrossRefGoogle Scholar
  25. Many D, Burkhart M, Dimitropoulos X (2012) Fast private set operations with sepia. Technical Report 345Google Scholar
  26. Meriem T, Mahmoud B, Fabrice K (2014) An approach for developing an interoperability mechanism between cloud providers. Int J Space-Based Situated Comput 4(2):88–99CrossRefGoogle Scholar
  27. Oded G (2009) The foundations of cryptography-vol 2, basic applications. Cambridge University Press, CambridgeGoogle Scholar
  28. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: Stern J (ed) EUROCRYPT’99. LNCS, vol 1592. Springer, Berlin, pp 223–238Google Scholar
  29. Ren W, Huang S, Ren Y, Choo KR (2016a) LiPISC: a lightweight and flexible method for privacy-aware intersection set computation. PLOS One. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157752
  30. Ren W, Liu R, Lei M, Choo KR (2016b) SeGoAC: a tree-based model for self-defined and group-oriented access control in mobile cloud computing. Comput Stand Interfaces. doi: 10.1016/j.csi.2016.09.001
  31. Wang Y, Du J, Cheng X, Liu Z, Lin K (2016) Degradation and encryption for outsourced png images in cloud storage. Int J Grid Util Comput 7(1):22–28CrossRefGoogle Scholar
  32. Xia Z, Wang X, Sun X, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(2):340–352CrossRefGoogle Scholar
  33. Zhu S, Yang X (2015) Protecting data in cloud environment with attribute-based encryption. Int J Grid Util Comput 6(2):91–97CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Xu An Wang
    • 1
    • 2
  • Fatos Xhafa
    • 3
  • Xiaoshuang Luo
    • 1
  • Shuaiwei Zhang
    • 1
  • Yong Ding
    • 2
  1. 1.Key Laboratory of Information and Network SecurityEngineering University of Chinese Armed Police ForceXi’anPeople’s Republic of China
  2. 2.Guangxi Key Laboratory of Cryptography and Information SecurityGuilin University of Electronic TechnologyGuilinPeople’s Republic of China
  3. 3.Department of Computer ScienceUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations