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Adaptive Framework for Privacy Preserving in Online Social Networks

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Abstract

In our current digital life, social networks play a vital role to share the data. A group of hazards that related to the privacy parameters settings of the universal online social network like facebook. The settings corresponding to the user’s shared data as well as the data sharing with privacy. A middleware server can split the users with regular social relationships within a same group. The community detection methodology normally request for the full access to the detailed social communications within the users. It is very sensitive to share the personal information like personal images which cause the uncertainty in case of privacy. In this paper, we suggest an Adaptive Framework for privacy preserving in Online Social Networks. It also affords the flexibility in which a third-party server can frequently choose the related sub graph. The emotional detection is constructed with the neuro-fuzzy technique that the fundamental emotions are identified through the singular value decomposition and produces the output of 22 emotions. The experimental results provide the real-time dataset that demonstrate the non-aggregated analysis and practical suggestions highlight the interferences to encourage the secured online social network usage.

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References

  1. Dehuria, S., Royb, R., Choe, S. B., & Ghosh, A. (2012). An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. Journal of Systems and Software, 85, 1333–1345.

    Article  Google Scholar 

  2. Chen, C., Wen, S., Zhang, J., Xiang, Y., Oliver, J., Alelaiwi, A., & Hassan, M. M. (2017). Investigating the deceptive information in Twitter spam. Future Generation Computer System, 72, 319–326.

    Article  Google Scholar 

  3. Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685.

    Article  Google Scholar 

  4. Qiu, T., Chen, N., Li, K., Qiao, D., & Fu, Z. (2017). Heterogeneous ad hoc networks: architectures, advances and challenges. Ad Hoc Networks, 55, 143–152.

    Article  Google Scholar 

  5. Krishna, K., & Murty, M. N. (1999). Genetic K-means algorithm. Transcation System, Man and Cybermetics Part B: (Cybermetics), 29, 433–439.

    Article  Google Scholar 

  6. He, Z., Cai, Z., Yu, J., Wang, X., Sun, Y., & Li, Y. (2017). Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Transactions on Vehicular Technology, 66(3), 2789–2800.

    Article  Google Scholar 

  7. Kudova, P. (2007) Clustering genetic algorithm. Proceedings of the 18th International Workshop on Database and Expert Systems Applications, September 3–7, ACM New York, USA., p. 138–142.

  8. Lin, H. J., Yang, F. W., & Kao, Y. T. (2005). An efficient GA based clustering technique. Tamkang Journal of Science and Engineering, 8, 113–122.

    Google Scholar 

  9. Lipczak, M., Milios. E. (2009). Agglomerative genetic algorithm for clustering m social networks. Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Montreal, QC, Canada, p. 1243–1250.

  10. Liu, Y.G., Chen K.F., Li, XM. (2004). A hybrid genetic based clustering algorithm. Proceedings of International Conference on Machine Learning and Cybernetics, Volume 3, August 26–29, China p. 1677–1682.

  11. Lu, Y., Lu, S., Fotouhi, F., Deng, Y., & Brown, S. J. (2004). Incremental genetic K-Means algorithm and its application in gene expression data analysis. Journal of BMC Bioinformatics. https://doi.org/10.1186/1471-2105-5-172

    Article  Google Scholar 

  12. Lu, Y., Lu, S., Fotouhi, F., Deng, Y., Brown, S.L. (2004b). FGKA: A fast genetic K-Means Clustering algorithm. Proceedings of the ACM Symposium on Applied Computing, ACM New York, USA, p. 622–623.

  13. Tang, L., Liu, H. (2009). Relational learning via latent social dimensions. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, France, p. 817–826.

  14. Uchida, M. Shibata, N. (2006). Extracting and visualization of an emerging topic from the blogspace. Proceedings of the 20th Annual Conference of the Japanese Society for Artifical Inteligence, ACM New York, USA.

  15. Wang, H., Chen, J., & Guo, K. (2011). A genetic spectral clustering algorithm. Journal of Computer Information System, 7, 3245–3252.

    Google Scholar 

  16. Wu, F.X. (2008). Genetic weighted K-Means algorithm for clustering large-scale gene expression data. BMC Bio information, 10.l186/1471–2105–9-S6-Sl 2.

  17. Zhou, X., Wang, X., Dougherty, E. R., Russ, D., & Suh, E. (2004). Gene clustering based on clusterwide mutual information. Journal of Computational Biology, 11, 147–161.

    Article  Google Scholar 

  18. Fortunato, S., & Hric, D. (2016). Community detection in networks: a user guide. Physics Reports, 659, 1–44.

    Article  MathSciNet  Google Scholar 

  19. Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Network, 5(2), 109–137.

    Article  MathSciNet  Google Scholar 

  20. Zhang, S., Liu, Q., & Lin, Y. (2017). Anonymizing popularity in online social networks with full utility. Future Genereration Computer Systems, 72, 227–238.

    Article  Google Scholar 

  21. Zheng, X., Cai, Z., Yu, J., Wang, C., & Li, Y. (2017). Follow but no track: Privacy preserved profile publishing in cyber-physical social systems. IEEE Internet of Things Journal, 4(6), 1868–1878.

    Article  Google Scholar 

  22. Dwork, C., McSherry, F., Nissim, K., Smith, A., (2006) Calibrating noise to sensitivity in private data analysis, in: Theory of Cryptography Conference, Springer, p. 265–284.

  23. Y. Zhang, E. Levina, J. Zhu, Community detection in networks with node features, 2015, arXiv preprint.

  24. Zhang, C., Zhang, K., Yuan, Q., Zhang, L., Hanratty, T., Han, J. (2016). GMove: Grouplevel mobility modeling using geo-tagged social media, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, p. 1305–1314.

  25. Salamatian, S., Zhang, A., du Pin Calmon, F., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P., Taft, N., (2015). Managing your private and public data: Bringing down inference attacks against your privacy, IEEE J. Sel. Top. Sign Processing. p 1240–1255.

  26. Zhao, S., Ke, W., Chen, J., Liu, F., Huang, M., Zhang, Y., & Tang, J. (2015). Tolerance granulation based community detection algorithm. Tsinghua Science Technology, 20(6), 620–626.

    Article  Google Scholar 

  27. Peng, C. Kolda, T.G., Pinar, A. (2014). Accelerating community detection by using k-core subgraphs, arXiv preprint .

  28. Yang, J., & Leskovec, J. (2014). Overlapping communities explain core–periphery organization of networks. Proceedings of the IEEE, 102(12), 1892–1902.

    Article  Google Scholar 

  29. Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal Complex Network, 3(2), 221–248.

    Article  MathSciNet  Google Scholar 

  30. Newman, M. E., & Clauset, A. (2016). Structure and inference in annotated networks. Nature Communication, 7(1), 1–1.

    Article  Google Scholar 

  31. Yang, J. McAuley, Leskovec, J. (2013). Community detection in networks with node attributes, in: Data Mining (ICDM), 2013 IEEE 13th international conference on, IEEE, p. 1151–1156.

  32. Such, J. M., & Criado, N. (2016). Resolving multi-party privacy conflicts in social media. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1851–1863.

    Article  Google Scholar 

  33. Ma, X., Ma, J., Li, H., Jiang, Qi., & Gao, S. (2018). ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks. Future Generation Computer Systems, 79, 82–94.

    Article  Google Scholar 

  34. Ma, X., Li, H., Ma, J., Jiang, Q., Gao, S., Xi, N., & Lu, D. (2017). Applet: A privacy-preserving framework for location-aware recommender system. Science China Information Sciences, 60(9), 092101–677475.

    Article  Google Scholar 

  35. Kökciyan, N., & Yolum, P. (2016). PriGuard: A semantic approach to detect privacy violations in online social networks. IEEE Transactions on Knowledge and Data Engineering, 28(10), 2724–2737.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, University College of Engineering (BIT Campus), Anna University, Trichirappalli, Tamilnadu, 620 024, India

    V. M. Priyadharshini

  2. Department of Computer Applications, University College of Engineering (BIT Campus), Anna University, Trichirappalli, Tamilnadu, 620 024, India

    A. Valarmathi

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  1. V. M. Priyadharshini
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  2. A. Valarmathi
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Correspondence to V. M. Priyadharshini.

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Priyadharshini, V.M., Valarmathi, A. Adaptive Framework for Privacy Preserving in Online Social Networks. Wireless Pers Commun 121, 2273–2290 (2021). https://doi.org/10.1007/s11277-021-08822-4

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  • DOI: https://doi.org/10.1007/s11277-021-08822-4

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