Total views : 71

Social Networking for Data Preservation: A Study


  • UIET, Kurukshetra University, Kurukshetra – 136119, Haryana, India


Objectives: This paper provides an overview of social network, privacy preservation in social networking, using kanonymity and L-diversity. Methods/Statistical Analysis: K-anonymity, L-diversity is used for social networking. In k-anonymity privacy micro data requires that each class contains at least K records. But k anonymity doesn't prevent attribute disclosure. To solve this I-diversity has been used. Practically L-diversity has been used and can be implemented efficiently. Findings: As privacy is the major concern in online social networking, so research in this field is going continuously. Methods like tabular micro data has been proposed by number of authors that provides solutions for privacy concern. But this method cannot be applied directly because social networking consists of number of edges and nodes. To provide better privacy and security for social networking L-diversity and K-diversity are used in combination. Improvement is done by using t-closeness technique. Many applications needs to publish data in binary form so there is a need to develop techniques that can preserve privacy of dynamic release.


Edges, K-Anonymity, L-Diversity, Nodes, Privacy, Security, Social Networks.

Full Text:

 |  (PDF views: 87)


  • Annapoorani A, Priya PI. Inferring Private Information from Social Network using Collective Classification, International Journal of Innovative Research in Computer and Communication Engineering. 2014 Mar; 2(1):1851– 58.
  • Singh A, Bansal D, Sofat S. Privacy Preserving Techniques in Social Networks Data Publishing: A Review, International Journal of Computer Applications. 2014 Jan; 87(15):9–15. CrossRef.
  • Machanavajjhala A, Kifer D, Gehrke J, Venkitasubramaniam M. l-Diversity Privacy Beyond K-Anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD). 2007 Mar; 1(1):1–52.
  • Tripathy BK, Mitra A. An Algorithm to Achieve K-Anonymity and L-Diversity Anonymisation in Social Networks. 2012 Fourth International Conference on In Computational Aspects of Social Networks (CASoN); 2012 Nov, p. 126–31.
  • Zhou B, Pei J. The K-Anonymity and L-Diversity Approaches for Privacy Preservation in Social Networks Against Neighborhood Attacks, Knowledge and Information Systems. 2011 Jul; 28(1):47–77. CrossRef.
  • Fung B, Jin YA, Li J. Preserving Privacy and Frequent Sharing Patterns for Social Network Data Publishing. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining; 2013 Aug, p. 479–85. CrossRef.
  • Prasad A, Panda GK, Mitra A, Singh A, Gour D. Applying l-Diversity in Anonymizing Collaborative Social Network, International Journal of Computer Science and Information Security. 2010 Jul; 8(2):324–29.
  • Jena L, Kamila N, Mishra S. Optimizing the Convergence of Data Utility and Privacy in Data Mining, International Journal of Application or Innovation in Engineering and Management. 2013 Jan; 2(1):155–66.
  • Jaganraj L, Balamurugan S. Empirical Investigation on Certain Anonymization Strategies for Preserving Privacy of Social Network Data, International Journal of Emerging Technology and Advanced Engineering. 2013 Oct; 3(10):55–63.
  • Li N, Li T, Venkata Subramanian S. T-Closeness Privacy beyond K-Anonymity and l-Diversity. IEEE 23rd International Conference; 2007 Apr, 15(2), p. 106–15.
  • Sweeney L. K-Anonymity a Model for Protecting Privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2002 Oct; 10(5):557–70. CrossRef.
  • Rashmi B, Suganya J. Anonymization Methodology for Sensitive Labels Protection in Social Network, IJREAT International Journal of Research in Engineering and Advanced Technology. 2014 May; 2(2):1–5.
  • Gaurav PR, Gururaj T. Anonymization: Enhancing Privacy and Security of Sensitive Data of Online Social Networks, International Journal of Computer Science and Information Technologies. 2014; 5(4):5995–6000.
  • Yuan M, Chen L. Semi-Edge Anonymity Graph Publication when the Protection Algorithm. In: International Conference on Database Systems for Advanced Applications; 2012 Apr, 15, p. 367–81. CrossRef. PMCid:PMC3282862.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.