Skip to main content

A Personalized Privacy Preserving Method for Publishing Social Network Data

  • Conference paper
Theory and Applications of Models of Computation (TAMC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8402))


One of the most important concerns in publishing social network data for social science research and business analysis is to balance between the individual’s privacy protection and data utility. Recently, researchers have developed lots of privacy models and anonymous techniques to prevent re-identifying of relevant information of nodes through structure information of social networks, but most of the existing methods did not cater for the individuals’ personalized privacy requirements and did not take full advantage of distributed characteristics of the social network nodes. Motivated by this, we specify three types of privacy attributes for various individuals and develop a personalized k-degree-l-diversity (PKDLD) anonymity model. Furthermore, we design and implement a graph anonymization algorithm with less distortion to the properties of the original graph. Finally, we conduct experiments on some real-world datasets to evaluate the practical efficiency of our methods, and the experimental results show that our algorithm reduces the anonymous cost efficiently and improves the data utility.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Zhou, B., Pei, J., Luk, W.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explorations Newsletter 10(2), 12–22 (2008)

    Article  Google Scholar 

  2. Wasserman, S.: Social network analysis: Methods and applications, vol. 8. Cambridge University Press (1994)

    Google Scholar 

  3. Yuan, M., Chen, L., Yu, P., Yu, T.: Protecting sensitive labels in social network data anonymization. ACM SIGKDD Explorations Newsletter 25(3), 633–647 (2013)

    Google Scholar 

  4. 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 

  5. Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 506–515. IEEE (2008)

    Google Scholar 

  6. Zhou, B., Pei, J.: The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems 28(1), 47–77 (2011)

    Article  MathSciNet  Google Scholar 

  7. Yuan, M., Chen, L., Yu, P.S.: Personalized privacy protection in social networks. Proceedings of the VLDB Endowment 4(2), 141–150 (2010)

    Google Scholar 

  8. Zou, L., Chen, L., Özsu, M.T.: K-automorphism: A general framework for privacy preserving network publication. Proceedings of the VLDB Endowment 2(1), 946–957 (2009)

    Google Scholar 

  9. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. ACM SIGCOMM Computer Communication Review 29, 251–262 (1999)

    Article  Google Scholar 

  10. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42. ACM (2007)

    Google Scholar 

  11. Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  12. Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information. In: PODS 1998, p. 188 (1998)

    Google Scholar 

  13. Sweeney, L.: k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(05), 557–570 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  14. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1), 3 (2007)

    Article  Google Scholar 

  15. Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, ICDE 2007, pp. 106–115. IEEE (2007)

    Google Scholar 

  16. Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. Computer Science Department Faculty Publication Series, p. 180 (2007)

    Google Scholar 

  17. Campan, A., Truta, T.M.: Data and structural k-anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PinKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Xiao, X., Tao, Y.: Anatomy: Simple and effective privacy preservation. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 139–150, VLDB Endowment (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Jiao, J., Liu, P., Li, X. (2014). A Personalized Privacy Preserving Method for Publishing Social Network Data. In: Gopal, T.V., Agrawal, M., Li, A., Cooper, S.B. (eds) Theory and Applications of Models of Computation. TAMC 2014. Lecture Notes in Computer Science, vol 8402. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06088-0

  • Online ISBN: 978-3-319-06089-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics