Efficient Private Matching Scheme for Friend Information Exchange

  • Fang Qi
  • Wenbo WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)


In the recent years, with the rapid development of social networks and mobile devices, mobile users can exchange the information and find the potential friends in vicinity through comparing the similarity degree between their personal attributes and make a connection via Wi-Fi/Bluetooth. The personal attributes, however, usually contain some private information, and users are not willing to reveal these to others in the process of friend discovery. In this paper, we proposed a novel efficient private matching scheme, which adopts an asymmetric scalar-preserving encryption according to the idea of k-nearest neighbor (kNN) queries. The personal profile of users will be processed in different ways, which is not recoverable. Moreover, our scheme relies on no Trusted Third Party (TTP). Detailed security and performance analysis demonstrate that our scheme can protect users’ private information and resist outside attack during the matching process.


Profile matching Privacy-preserving Information exchange Asymmetric scalar-preserving encryption PMSNs 



This work is supported by the National Natural Science Foundation of China under Grant No. 61103035 and the Science and Technology Program of Hunan Province under Grant No. 2014GK3029.


  1. 1.
    Boneh, D., Goh, E.-J., Nissim, K.: Evaluating 2-DNF formulas on ciphertexts. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 325–341. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  2. 2.
    De Cristofaro, E., Kim, J., Tsudik, G.: Linear-complexity private set intersection protocols secure in malicious model. In: Abe, M. (ed.) ASIACRYPT 2010. LNCS, vol. 6477, pp. 213–231. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  3. 3.
    Dong, W., Dave, V., Qiu, L., Zhang, Y.: Secure friend discovery in mobile social networks. In: 2011 IEEE INFOCOM, pp. 1647–1655. IEEE (2011)Google Scholar
  4. 4.
    Freedman, M.J., Nissim, K., Pinkas, B.: Efficient private matching and set intersection. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 1–19. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  5. 5.
    Ioannidis, I., Grama, A., Atallah, M.J.: A secure protocol for computing dotproducts in clustered and distributed environments. In: Proceedings of 2002 International Conference on Parallel Processing, pp. 379–384. IEEE (2002)Google Scholar
  6. 6.
    Kissner, L., Song, D.: Privacy-preserving set operations. In: Shoup, V. (ed.) CRYPTO 2005. LNCS, vol. 3621, pp. 241–257. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Li, M., Cao, N., Yu, S., Lou, W.: FindU: privacy-preserving personal profile matching in mobile social networks. In: 2011 IEEE INFOCOM, pp. 2435–2443. IEEE (2011)Google Scholar
  8. 8.
    Liu, Q., Wang, G., Wu, J.: Time-based proxy re-encryption scheme for secure data sharing in a cloud environment. Inf. Sci. 258, 355–370 (2014)CrossRefGoogle Scholar
  9. 9.
    Lu, R., Lin, X., Liang, X., Shen, X.: A secure handshake scheme with symptoms matching for mHealthcare social network. Mob. Netw. Appl. 16(6), 683–694 (2011)CrossRefGoogle Scholar
  10. 10.
    Lu, R., Lin, X., Shen, X.: SPOC: a secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency. IEEE Trans. Parallel Distrib. Syst. 24(3), 614–624 (2013)CrossRefGoogle Scholar
  11. 11.
    Manweiler, J., Scudellari, R., Cox, L.P.: Smile: encounter-based trust for mobile social services. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, pp. 246–255. ACM (2009)Google Scholar
  12. 12.
    Pietiläinen, A.K., Oliver, E., LeBrun, J., Varghese, G., Diot, C.: MobiClique: middleware for mobile social networking. In: Proceedings of the 2nd ACM Workshop on Online Social Networks, pp. 49–54. ACM (2009)Google Scholar
  13. 13.
    Rane, S., Sun, W., Vetro, A.: Privacy-preserving approximation of L1 distance for multimedia applications. In: 2010 IEEE International Conference on Multimedia and Expo (ICME), pp. 492–497 (2010)Google Scholar
  14. 14.
    Sang, Y., Shen, H.: Efficient and secure protocols for privacy-preserving set operations. ACM Trans. Inf. Syst. Secur. (TISSEC) 13(1), 9 (2009)CrossRefGoogle Scholar
  15. 15.
    Von Arb, M., Bader, M., Kuhn, M., Wattenhofer, R.: VENETA: serverless friend-of-friend detection in mobile social networking. In: IEEE International Conference on Wireless and Mobile Computing Networking and Communications (WIMOB 08), pp. 184–189. IEEE (2008)Google Scholar
  16. 16.
    Wang, G., Musau, F., Guo, S., Abdullahi, M.B.: Neighbor similarity trust against sybil attack in P2P e-commerce. IEEE Trans. Parallel Distrib. Syst. 26(3), 824–833 (2015)CrossRefGoogle Scholar
  17. 17.
    Wang, Y., Xu, J.: Overview on privacy-preserving profile-matching mechanisms in mobile social networks in proximity (MSNP). In: 2014 Ninth Asia Joint Conference on Information Security (ASIA JCIS), pp. 133–140. IEEE (2014)Google Scholar
  18. 18.
    Xie, Q., Hengartner, U.: Privacy-preserving matchmaking for mobile social networking secure against malicious users. In: 2011 Ninth Annual International Conference on Privacy, Security and Trust (PST), pp. 252–259. IEEE (2011)Google Scholar
  19. 19.
    Zhang, R., Zhang, J., Zhang, Y., Sun, J., Yan, G.: Privacy-preserving profile matching for proximity-based mobile social networking. IEEE J. Sel. Areas Commun. 31(9), 656–668 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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