Advertisement

Defend the Clique-based Attack for Data Privacy

  • Meng Han
  • Dongjing Miao
  • Jinbao Wang
  • Liyuan Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11346)

Abstract

Clique, as the most compact cohesive component in a graph, has been employed to identify cohesive subgroups of entities and explore the sensitive information in the online social network, crowdsourcing network, and cyber physical network, etc. In this study, we focus on the defense of clique-based attack and target at reducing the risk of entities security/privacy issues in clique structure. Since the ultimate resolution for defending the clique-based attack and risk is wrecking the clique with minimum cost, we establish the problem of clique-destroying (CD) in the network from a fundamental algorithm aspect. Interestingly, we notice that the clique-destroying problem in the directed graph is still an unsolved problem, and complexity analysis also does not exist. Therefore, we propose an innovative formal clique-destroying problem and proof the NP-complete problem complexity with solid theoretical analysis, then present effective and efficient algorithms for both undirected and directed graph. Furthermore, we show how to extend our algorithm to data privacy protection applications with controllable parameter k, which could adjust the size of a clique we wish to destroy. By comparing our algorithm with the up-to-date anonymization approaches, the real data experiment demonstrates that our resolution could efficaciously defend the clique-based security and privacy attacks.

Notes

Acknowledgement

This work is partly supported by the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018BDKFJJ002) and the National Science Foundation (NSF) under grant NOs. 1252292, 1741277, 1704287, and 1829674.

References

  1. 1.
    Zhang, J., Li, Q., Schooler, E.M.: iHEMS: an information-centric approach to secure home energy management. In: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 217–222. IEEE (2012)Google Scholar
  2. 2.
    Aberer, K., Alonso, G., Kossmann, D.: Data management for a smart earth: the swiss NCCR-MICS initiative. ACM SIGMOD Rec. 35(4), 40–45 (2006)CrossRefGoogle Scholar
  3. 3.
    Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014)CrossRefGoogle Scholar
  4. 4.
    Liang, Y., Cai, Z., Han, Q., Li, Y.: Location privacy leakage through sensory data. Secur. Commun. Netw. 2017, 1–12 (2017)CrossRefGoogle Scholar
  5. 5.
    Zheng, X., Cai, Z., Li, J., Gao, H.: Location-privacy-aware review publication mechanism for local business service systems. In: INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)Google Scholar
  6. 6.
    He, Z., Cai, Z., Yu, J., Wang, X., Sun, Y., Li, Y.: Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans. Veh. Technol. 66(3), 2789–2800 (2017)CrossRefGoogle Scholar
  7. 7.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)Google Scholar
  8. 8.
    Potharaju, R., Carbunar, B., Nita-Rotaru, C.: iFriendU: leveraging 3-cliques to enhance infiltration attacks in online social networks. In: Proceedings of the 17th ACM Conference on Computer and Communications Security. ACM (2010) 723–725Google Scholar
  9. 9.
    Gulyás, G.G., Simon, B., Imre, S.: An efficient and robust social network de-anonymization attack. In: Proceedings of the 2016 ACM on Workshop on Privacy in the Electronic Society, pp. 1–11. ACM (2016)Google Scholar
  10. 10.
    Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-79228-4_1CrossRefzbMATHGoogle Scholar
  11. 11.
    Niu, B., Li, Q., Zhu, X., Cao, G., Li, H.: Achieving k-anonymity in privacy-aware location-based services. In: 2014 Proceedings IEEE INFOCOM, pp. 754–762. IEEE (2014)Google Scholar
  12. 12.
    Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321. ACM (2015)Google Scholar
  13. 13.
    Dalenius, T.: Towards a methodology for statistical disclosure control. Statistik Tidskrift 15, 429–444 (1977)Google Scholar
  14. 14.
    Dwork, C., Naor, M.: On the difficulties of disclosure prevention in statistical databases or the case for differential privacy. J. Priv. Confid. 2(1), 8 (2008)Google Scholar
  15. 15.
    Yannakakis, M.: Edge-deletion problems. SIAM J. Comput. 10(2), 297–309 (1981)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Acquisti, A., Brandimarte, L., Loewenstein, G.: Privacy and human behavior in the age of information. Science 347(6221), 509–514 (2015)CrossRefGoogle Scholar
  19. 19.
    Young, A.L., Quan-Haase, A.: Privacy protection strategies on facebook: the internet privacy paradox revisited. Inf. Commun. Soc. 16(4), 479–500 (2013)CrossRefGoogle Scholar
  20. 20.
    Bettini, C., Riboni, D.: Privacy protection in pervasive systems: state of the art and technical challenges. Pervasive Mob. Comput. 17, 159–174 (2015)CrossRefGoogle Scholar
  21. 21.
    Zhao, J., Liu, J., Qin, Z., Ren, K.: Privacy protection scheme based on remote anonymous attestation for trusted smart meters. IEEE Trans. Smart Grid 9, 3313–3320 (2016)CrossRefGoogle Scholar
  22. 22.
    Naor, M., Nissim, K.: Communication preserving protocols for secure function evaluation. In: Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, pp. 590–599. ACM (2001)Google Scholar
  23. 23.
    Goldwasser, S.: Multi party computations: past and present. In: Proceedings of the Sixteenth Annual ACM Symposium on Principles of Distributed Computing, pp. 1–6. ACM (1997)Google Scholar
  24. 24.
    Han, M., Li, J., Cai, Z., Han, Q.: Privacy reserved influence maximization in GPS-enabled cyber-physical and online social networks. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp. 284–292. IEEE (2016)Google Scholar
  25. 25.
    Albinali, H., Han, M., Wang, J., Gao, H., Li, Y.: The roles of social network mavens. In: 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), pp. 1–8. IEEE (2016)Google Scholar
  26. 26.
    Cai, Z., Zheng, X.: A private and efficient mechanism for data uploading in smart cyber-physical systems. IEEE Trans. Netw. Sci. Eng. (2018)Google Scholar
  27. 27.
    Zheng, X., Luo, G., Cai, Z.: A fair mechanism for private data publication in online social networks. IEEE Trans. Netw. Sci. Eng. (2018)Google Scholar
  28. 28.
    Li, J., Cai, Z., Wang, J., Han, M., Li, Y.: Truthful incentive mechanisms for geographical position conflicting mobile crowdsensing systems. IEEE Trans. Comput. Soc. Syst. 5(2), 324–334 (2018)CrossRefGoogle Scholar
  29. 29.
    Han, M., Wang, J., Yan, M., Ai, C., Duan, Z., Hong, Z.: Near-complete privacy protection: cognitive optimal strategy in location-based services. Procedia Comput. Sci. 129, 298–304 (2018)CrossRefGoogle Scholar
  30. 30.
    Ling, X., Wu, C., Ji, S., Han, M.: H\(_{2}\)DoS: an application-layer DoS attack towards HTTP/2 protocol. In: Lin, X., Ghorbani, A., Ren, K., Zhu, S., Zhang, A. (eds.) SecureComm 2017. LNICST, vol. 238, pp. 550–570. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78813-5_28CrossRefGoogle Scholar
  31. 31.
    Han, M., Li, L., Peng, X., Hong, Z., Li, M.: Information privacy of cyber transportation system: opportunities and challenges. In: Proceedings of the 6th Annual Conference on Research in Information Technology, pp. 23-28. ACM (2017). https://dl.acm.org/citation.cfm?id=31256
  32. 32.
    Zheng, X., Cai, Z., Yu, J., Wang, C., Li, Y.: Follow but no track: privacy preserved profile publishing in cyber-physical social systems. IEEE Internet Things J. 4(6), 1868–1878 (2017)CrossRefGoogle Scholar
  33. 33.
    Zhou, Y., Han, M., Liu, L., He, J.S., Wang, Y.: Deep learning approach for cyberattack detection. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 262–267. IEEE (2018)Google Scholar
  34. 34.
    Han, M., Duan, Z., Li, Y.: Privacy issues for transportation cyber physical systems. In: Sun, Y., Song, H. (eds.) Secure and Trustworthy Transportation Cyber-Physical Systems. SCS, pp. 67–86. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-3892-1_4CrossRefGoogle Scholar
  35. 35.
    Joshi, A.P., Han, M., Wang, Y.: A survey on security and privacy issues of blockchain technology. Math. Found. Comput. 1(2), 121–147 (2018)CrossRefGoogle Scholar
  36. 36.
    Liu, L., Han, M., Wang, Y., Zhou, Y.: Understanding data breach: a visualization aspect. In: Chellappan, S., Cheng, W., Li, W. (eds.) WASA 2018. LNCS, vol. 10874, pp. 883–892. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-94268-1_81CrossRefGoogle Scholar
  37. 37.
    Liang, Y., Cai, Z., Yu, J., Han, Q., Li, Y.: Deep learning based inference of private information using embedded sensors in smart devices. IEEE Netw. 32(4), 8–14 (2018)CrossRefGoogle Scholar
  38. 38.
    Wang, J., Cai, Z., Li, Y., Yang, D., Li, J., Gao, H.: Protecting query privacy with differentially private k-anonymity in location-based services. Pers. Ubiquit. Comput. 22, 1–17 (2018)CrossRefGoogle Scholar
  39. 39.
    Dwork, C.: A firm foundation for private data analysis. Commun. ACM 54(1), 86–95 (2011)CrossRefGoogle Scholar
  40. 40.
    Dwork, C., Rothblum, G.N., Vadhan, S.: Boosting and differential privacy. In: 2010 51st Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 51–60. IEEE (2010)Google Scholar
  41. 41.
    Chaudhuri, K., Monteleoni, C.: Privacy-preserving logistic regression. In: Advances in Neural Information Processing Systems pp. 289–296 (2009)Google Scholar
  42. 42.
    Chaudhuri, K., Sarwate, A., Sinha, K.: Near-optimal differentially private principal components. In: Advances in Neural Information Processing Systems, pp. 989–997 (2012)Google Scholar
  43. 43.
    Sarwate, A.D., Chaudhuri, K.: Signal processing and machine learning with differential privacy: algorithms and challenges for continuous data. IEEE Sig. Process. Mag. 30(5), 86–94 (2013)CrossRefGoogle Scholar
  44. 44.
    Ho, S.S., Ruan, S.: Differential privacy for location pattern mining. In: Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 17–24. ACM (2011)Google Scholar
  45. 45.
    Dewri, R.: Local differential perturbations: location privacy under approximate knowledge attackers. IEEE Trans. Mob. Comput. 12(12), 2360–2372 (2013)CrossRefGoogle Scholar
  46. 46.
    Xiao, Y., Xiong, L.: Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1298–1309. ACM (2015)Google Scholar
  47. 47.
    Yildiz, H., Kruegel, C.: Detecting social cliques for automated privacy control in online social networks. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 353–359. IEEE (2012)Google Scholar
  48. 48.
    Pan, X., Xu, J., Meng, X.: Protecting location privacy against location-dependent attacks in mobile services. IEEE Trans. Knowl. Data Eng. 24(8), 1506–1519 (2012)CrossRefGoogle Scholar
  49. 49.
    Narayanan, A., Shi, E., Rubinstein, B.I.: Link prediction by de-anonymization: How we won the Kaggle social network challenge. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1825–1834. IEEE (2011)Google Scholar
  50. 50.
    Srivatsa, M., Hicks, M.: Deanonymizing mobility traces: using social network as a side-channel. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp. 628–637. ACM (2012)Google Scholar
  51. 51.
    Ji, S., Li, W., Srivatsa, M., He, J.S., Beyah, R.: Structure based data de-anonymization of social networks and mobility traces. In: Chow, S.S.M., Camenisch, J., Hui, L.C.K., Yiu, S.M. (eds.) ISC 2014. LNCS, vol. 8783, pp. 237–254. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-13257-0_14CrossRefGoogle Scholar
  52. 52.
    Gulyás, G.G., Imre, S.: Analysis of identity separation against a passive clique-based de-anonymization attack. Infocommunications J. 4(3), 11–20 (2011)Google Scholar
  53. 53.
    Niedermeier, R.: Invitation to Fixed-parameter Algorithms (2006)CrossRefGoogle Scholar
  54. 54.
    Brügmann, D., Komusiewicz, C., Moser, H.: On generating triangle-free graphs. Electron. Notes Discret. Math. 32, 51–58 (2009)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, Heidelberg (1972).  https://doi.org/10.1007/978-1-4684-2001-2_9CrossRefGoogle Scholar
  56. 56.
    Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)CrossRefGoogle Scholar
  57. 57.
    Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)MathSciNetCrossRefGoogle Scholar
  58. 58.
    Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)Google Scholar
  59. 59.
    Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003).  https://doi.org/10.1007/978-3-540-39718-2_23CrossRefGoogle Scholar
  60. 60.
    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. ACM (2010)Google Scholar
  61. 61.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370. ACM (2010)Google Scholar
  62. 62.
    Ji, S., Li, W., Mittal, P., Hu, X., Beyah, R.A.: SecGraph: a uniform and open-source evaluation system for graph data anonymization and de-anonymization. In: USENIX Security Symposium, pp. 303–318 (2015)Google Scholar
  63. 63.
    Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 739–750. SIAM (2008)Google Scholar
  64. 64.
    Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 93–106. ACM (2008)Google Scholar
  65. 65.
    Sala, A., Zhao, X., Wilson, C., Zheng, H., Zhao, B.Y.: Sharing graphs using differentially private graph models. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 81–98. ACM (2011)Google Scholar
  66. 66.
    Yartseva, L., Grossglauser, M.: On the performance of percolation graph matching. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 119–130. ACM (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Meng Han
    • 1
  • Dongjing Miao
    • 2
    • 3
  • Jinbao Wang
    • 3
  • Liyuan Liu
    • 1
  1. 1.Data-driven Intelligence Research (DIR) LaboratoryKennesaw State UniversityKennesawUSA
  2. 2.Georgia State UniversityAtlantaUSA
  3. 3.Harbin Institute of TechnologyHarbinChina

Personalised recommendations