European Symposium on Research in Computer Security

Computer Security -- ESORICS 2015 pp 61-80 | Cite as

Privacy-Preserving Link Prediction in Decentralized Online Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9327)


We consider the privacy-preserving link prediction problem in decentralized online social network (OSNs). We formulate the problem as a sparse logistic regression problem and solve it with a novel decentralized two-tier method using alternating direction method of multipliers (ADMM). This method enables end users to collaborate with their online service providers without jeopardizing their data privacy. The method also grants end users fine-grained privacy control to their personal data by supporting arbitrary public/private data split. Using real-world data, we show that our method enjoys various advantages including high prediction accuracy, balanced workload, and limited communication overhead. Additionally, we demonstrate that our method copes well with link reconstruction attack.


Distributed algorithms ADMM Mobile computing Privacy Social networks 



This work was supported by US National Science Foundation under grants CNS-1405747, CNS-1156318, CNS-1443889, and CSR-1217889.


  1. 1.
    Google finance. Accessed 19 June 2014.
  2. 2.
    Statistic brain. Accessed 20 June 2014.
  3. 3.
    Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th International Conference on World Wide Web, pp. 531–540 (2009)Google Scholar
  4. 4.
    Facebook. Data use policy. Accessed 25 June 2014.
  5. 5.
    Dodson, B., Vo, I., Purtell, T., Cannon, A., Lam, M.: Musubi: disintermediated interactive social feeds for mobile devices. In: Proceedings of the 21st International Conference On World Wide Web, pp. 211–220 (2012)Google Scholar
  6. 6.
    Diaspora Inc. Accessed 28 May 2014.
  7. 7.
    Omlet Inc. Accessed 28 May 2014.
  8. 8.
    Aggarwal, C.C.: Social Network Data Analytics. Springer, US (2011)CrossRefMATHGoogle Scholar
  9. 9.
    Datta, A., Buchegger, S., Vu, L.-H., Strufe, T., Rzadca, K.: Decentralized online social networks. In: Furht, B. (ed.) Handbook of Social Network Technologies and Applications, pp. 349–378. Springer, US (2010)CrossRefGoogle Scholar
  10. 10.
    Baden, R., Bender, A., Spring, N., Bhattacharjee, B., Starin, D.: Persona: an online social network with user-defined privacy. ACM SIGCOMM Comput. Commun. Rev. 39(4), 135–146 (2009)CrossRefGoogle Scholar
  11. 11.
    Cutillo, L.A., Molva, R., Strufe, T.: Safebook: a privacy-preserving online social network leveraging on real-life trust. IEEE Commun. Mag. 47(12), 94–101 (2009)CrossRefGoogle Scholar
  12. 12.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 635–644 (2011)Google Scholar
  13. 13.
    Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 539–547 (2012)Google Scholar
  14. 14.
    Fisher, D.: Using egocentric networks to understand communication. IEEE Internet Comput. 9(5), 20–28 (2005)CrossRefGoogle Scholar
  15. 15.
    Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  16. 16.
    Yu, H., Jiang, X., Vaidya, J.: Privacy-preserving svm using nonlinear kernels on horizontally partitioned data. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 603–610. ACM (2006)Google Scholar
  17. 17.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412 (2004)Google Scholar
  18. 18.
    Al Hasan, M., Chaoji, V., Salem, S., Zaki, M.: Link prediction using supervised learning. In: Proceedings of SDM Workshop on Link Analysis, Counter-terrorism and Security (2006)Google Scholar
  19. 19.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650 (2010)Google Scholar
  20. 20.
    Kim, M., Leskovec, J.: Latent multi-group membership graph model. In: Proceedings of the 29th International Conference on Machine Learning, pp. 1719–1726 (2012)Google Scholar
  21. 21.
    Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798 (2007)Google Scholar
  22. 22.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009) CrossRefMATHGoogle Scholar
  23. 23.
    Hinton, G., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)Google Scholar
  25. 25.
    Glowinski, R., Marroco, A.: Sur l’approximation, par éléments finis d’ordre un, et la résolution, par pénalisation-dualité d’une classe de problèmes de dirichlet non linéaires. ESAIM: Math. Model. Numer. Anal. Modélisation Math. Anal. Numérique 9(R2), 41–76 (1975)MATHGoogle Scholar
  26. 26.
    Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput. Math. Appl. 2(1), 17–40 (1976)CrossRefMATHGoogle Scholar
  27. 27.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)CrossRefMATHGoogle Scholar
  28. 28.
    Parikh, N., Boyd, S.: Proximal algorithms. Found. Trends Optim. 1(3), 123–231 (2013)Google Scholar
  29. 29.
    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16(5), 1190–1208 (1995)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (2013)MATHGoogle Scholar
  32. 32.
    West, R., Paskov, H.S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. Trans. Assoc. Comput. Linguist. 2, 297–310 (2014)Google Scholar
  33. 33.
    Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1 (2014).
  34. 34.
    Gurobi Optimization Inc., Gurobi optimizer reference manual, version 5.6 (2014).
  35. 35.
    Levin, D.A., Peres, Y., Wilmer, E.L.: Markov Chains and Mixing Times. American Mathematical Society, Providence (2009)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yao Zheng
    • 1
  • Bing Wang
    • 1
  • Wenjing Lou
    • 1
  • Y. Thomas Hou
    • 1
  1. 1.Virginia Polytechnic Institute and State UniversityBlacksburgUSA

Personalised recommendations