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An Approach of Privacy Preserving based Publishing in Twitter

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Published:09 September 2014Publication History

ABSTRACT

With the increase in online publishing of social network data the requirement to protect confidential information related to users has become the main concern of publishers. To cater to this need many anonymization techniques like K-anonymity, L-diversity and T-closeness has been proposed by various researchers for micro-data as well as social network data. In this paper we aim to protect sensitive information of users of Twitter-second most popular social networking site. For the purpose of carrying out anonymization, a crawler has been developed to collect data of around 10K users from publicly available information. Data of around 30 users have been used to carry out the experimental work using ARX tool. All three anonymization methods: K-anonymity, L-diversity and T-closeness have been used. Performance of technique is evaluated using information gain as a metric.

References

  1. Boyd D.M. and Ellison N. B. 2007. Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication. 13, 1 (17 Dec 2007), 210--230. DOI= 10.1111/j.1083-6101.2007.00393.x.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Zhou B., Pei J. and Luk W. 2008. A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explorations Newsletter. 10, 2, 12--22. DOI= 10.1145/1540276.1540279. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Rosenblum D. 2007. What anyone can know: The privacy risks of social networking sites. IEEE Security & Privacy. (4-June-2007), 5, 3, 40--49. DOI= 10.1109/MSP.2007.75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Chen B., Kifer D., LeFevre K. and Machanavajjhala A. 2009. Privacy-Preserving Data Publishing. Foundations and Trends in Databases. 2, 1--2. DOI= 10.1561/1900000008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Narayanan A. and Shmatikov V. 2009. De-anonymizing social networks. In Proceedings of 30th IEEE Symposium on Security and Privacy, IEEE Computer Society Washington, DC, USA,173--187. DOI= 10.1109/SP.2009.22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Shi P., Xiong L. and Fung B. 2010. Anonymizing Data with Quasi-Sensitive Attribute Values. In Proceedings of the 19th ACM international conference on Information and knowledge management CIKM '10, ACM, New York, NY, USA, 1389--1392. DOI= 10.11.45/1871437.1871628. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Wei Q. and Lu Y. 2008. Preservation of Privacy in Publishing Social Network Data. In Proceedings of International Symposium on Electronic Commerce and Security, Guangzhou City. 421--425. DOI= 10.1109/ISECS.2008.112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Liu K. and Terzi E. 2008. Towards identity anonymization on graphs. In Proceedings of 2008 ACM SIGMOD International conference on Management of data, Vancouver, Canada.93--106. DOI= 10.1145/1376616.1376629. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bai K.Liu Y., and Liu P. Prevent Identity Disclosure in Social Network Data Study. ACM.Google ScholarGoogle Scholar
  10. Zou L., Chen L., and Ozsu M. 2009. K-automorphism: A general framework for privacy preserving network publication. In Journal Proceedings of the VLDB Endowment. 2, 1, 946--957. DOI= 10.14778/1687627.1687734. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Tripathy B. and Panda G. 2010. A New Approach to Manage Security against Neighborhood Attacks in Social Networks. In Proceedings of International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Odense, 264--269. DOI= 10.1109/ASONAM.2010.69. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Cheng J., Fu A., and Liu Z. 2010. K-isomorphism: privacy preserving network publication against structural attacks. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. 459--470. DOI= 10.1145/1807167.1807218. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Wu W., Xiao Y., Wang W., He Z., and Wang Z. 2010. k-symmetry model for identity anonymization in social networks. In Proceedings of the 13th International Conference on Extending Database Technology. ACM, New York, USA. 111--122. DOI= 10.1145/1739041.1739058. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Lan L., Hua Jin H., and Lu Y. 2011. Personalized anonymity in social networks data publication. In Proceedings of IEEE International Conference on Computer Science and Automation Engineering (CSAE). Shanghai, 479--482. DOI= 10.1109/CSAE.2011.5953265.Google ScholarGoogle Scholar
  15. Truta T., Campan A., and Ralescu A. 2012. Preservation of Structural Properties in Anonymized Social Networks. In Proceedings of 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom). Pittsburgh, PA. 619--627.Google ScholarGoogle Scholar
  16. Skarkala M., Maragoudakis M., Gritzalis S., Mitrou L., Toivonen H., Moen P. 2012. Privacy Preservation by K-Anonymization of weighted Social Networks. In IEEE Computer Society, ASONAM. 423--428. DOI= 10.1109/ASONAM.2012.75. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Panda G., Mitra A., Prasad A., Singh A., and Gour D. 2010. Applying l-Diversity in anonymizing collaborative social network. In: International Journal of Computer Science and Information Security. 8, 2. 324--329.Google ScholarGoogle Scholar
  18. Li N. Zhang N., and Das S. 2011. Relationship Privacy Preservation in Publishing Online Social Networks. In Proceedings of IEEE International Conference on Privacy, Security, Risk, and Trust. Boston, MA. 443--450. DOI= 10.1109/PASSAT/SocialCom.2011.191.Google ScholarGoogle Scholar
  19. Kavianpour S., Ismail Z., and Mohtaseb A. 2011. Preserving Identity of Users In Social Network Sites By Integrating Anonymization And Diversification Algorithms. In: International Journal of Digital Information and Wireless Communications (IJDIWC), Hongkong, 1, 1, 32--40.Google ScholarGoogle Scholar
  20. Tripathy B.and Mitra A. 2012. An Algorithm to achieve k-anonymity and l-diversity anonymisation in Social Networks. In Proceedings of Fourth International Conference on Computational Aspects of Social Networks (CASoN), Sao Carlos, 126--131. DOI= 0.1109/CASoN.2012.6412390.Google ScholarGoogle Scholar
  21. Yuan M.Lei Chen L. and Yu P., Yu T. 2013. Protecting Sensitive Labels in Social Network Data Anonymization. In: IEEE Transactions on Knowledge And Data Engineering, 25, 3, 633--647. DOI= 10.1109/TKDE.2011.259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Guha S., Tang K., and Francis P. 2008. NOYB: Privacy in Online Social Networks. In Proceedings of first workshop on Online social networks WOSN'08, ACM New York, NY, USA, 49--54. DOI= 10.1145/1397735.1397747. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Zhan J., Blosser G., Yang C., and Singh L. 2008. Privacy Preserving Collaborative Social Network. Intelligence and Security Informatics, Lecture Notes in Computer Science, 5075, 2008, 114--125. DOI= 10.1007/978-3-540-69304-8_13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Campan A., Truta T., and Cooper N. 2010. P-Sensitive K-Anonymity with Generalization Constraints. In: Transactions on Data Privacy. 3, 2, 65--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Zheleva E., and Getoor L. 2011. Privacy in Social Networks: A Survey. In: Social Network Data Analytics, Springer US, 277--306. DOI= 10.1007/978-1-4419-8462-3_10.Google ScholarGoogle Scholar
  26. Ford R., Truta T., and Campan A. P-Sensitive K-Anonymity for Social Networks.DOI= doi=10.1.1.148.4222.Google ScholarGoogle Scholar
  27. Lijie Z. and Weining Z. 2009. Edge Anonymity in Social Network Graphs. In Proceedings of International Conference on Computational Science and Engineering CSE '09(29--31 Aug. 2009), 1--8.DOI= 10.1109/CSE.2009.310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Ying X. and Wu X. 2009. On link privacy in randomizing social networks. In: Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 5476, 28--39. DOI= 10.1007/978-3-642-01307-2_6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Tootoonchian A., Saroiu S., Ganjali Y., and Wolman A. 2009. Lockr: Better Privacy for Social Networks. In Proceedings of the 5th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT). ACM, New York, NY, US, 169--180, DOI= 10.1145/1658939.1658959. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Fong P. Anwar M. and Zhen Zhao Z. 2009. A Privacy Preservation Model for Facebook-Style Social Network Systems. In: Computer Security - ESORICS 2009, Lecture Notes in Computer Science, 5789, 2009, 303--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Tang X. and Yang C. 2010. Generalizing Terrorist Social Networks with K-Nearest Neighbor and Edge Betweenness for Social Network Integration and Privacy Preservation. In Proc. of IEEE International Conference on Intelligence and Security Informatics. DOI= 10.1109/ISI.2010.5484776.Google ScholarGoogle Scholar
  32. Lan L. and Jin S. 2010. Anonymizing Social Network using Bipartite Graph. In Proceedings of International Conference on Computational and Information Sciences (ICCIS), Chengdu, 993--996. DOI= 10.1109/ICCIS.2010.245. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Ding X., Zhang L., Wan Z. and Gu M. 2010. A Brief Survey on De-anonymization Attacks in Online Social Networks. In Proceedings of International Conference on Computational Aspects of Social Networks, Taiyuan, 611--615. DOI= 10.1109/CASoN.2010.139. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Samarati P. and L. Sweeney L. 2001. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression. In: IEEE Transactions on Knowledge and Data Engineering. DOI= doi=10.1.1.37.5829.Google ScholarGoogle Scholar
  35. Machanavajjhala A., Kifer D., Gehrke J. 2007. **l-diversity: Privacy beyond k-anonymity. In ACM Transactions on Knowledge Discovery from Data (TKDD). 1,1. DOI= 10.1145/1217299.1217302. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Li N., Li T. and Venkatasubramanian S. 2007. t-closeness: Privacy beyond k-anonymity and 1-diversity. In Proceedings of 23rd International Conference on Data Engineering ICDE 2007. IEEE, Istanbul, 106--115. DOI= 10.1.1.92.587.Google ScholarGoogle Scholar
  37. Statistics of social media: http://expandedramblings.com/index.php/resource-how-many-people-use-the-top-social-media. Accessed: 2014-04-22.Google ScholarGoogle Scholar
  38. Twitter APIs: https://dev.twitter.com/docs/api/streaming. Accessed: 2014-02-14.Google ScholarGoogle Scholar
  39. ARX tool: http://arx.deidentifier.org/api/. Accessed: 2014-03-12.Google ScholarGoogle Scholar

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      cover image ACM Other conferences
      SIN '14: Proceedings of the 7th International Conference on Security of Information and Networks
      September 2014
      518 pages
      ISBN:9781450330336
      DOI:10.1145/2659651

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      Publication History

      • Published: 9 September 2014

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      SIN '14 Paper Acceptance Rate32of109submissions,29%Overall Acceptance Rate102of289submissions,35%

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