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k-Degree Anonymity Model for Social Network Data PublishingMACWAN, K. R. , PATEL, S. J.
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data privacy, data processing, publishing, social network services, utility programs
data(8), social(7), privacy(6), networks(6), preserving(5), network(5), information(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 117 - 124
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04014
Web of Science Accession Number: 000417674300014
SCOPUS ID: 85035757216
Publicly accessible platform for social networking has gained special attraction because of its easy data sharing. Data generated on such social network is analyzed for various activities like marketing, social psychology, etc. This requires preservation of sensitive attributes before it becomes easily accessible. Simply removing the personal identities of the users before publishing data is not enough to maintain the privacy of the individuals. The structure of the social network data itself reveals much information regarding its users and their connections. To resolve this problem, k-degree anonymous method is adopted. It emphasizes on the modification of the graph to provide at least k number of nodes that contain the same degree. However, this approach is not efficient on a huge amount of social data and the modification of the original data fails to maintain data usefulness. In addition to this, the current anonymization approaches focus on a degree sequence-based graph model which leads to major modification of the graph topological properties. In this paper, we have proposed an improved k-degree anonymity model that retain the social network structural properties and also to provide privacy to the individuals. Utility measurement approach for community based graph model is used to verify the performance of the proposed technique.
|References|||||Cited By «-- Click to see who has cited this paper|
| K. Liu and E. Terzi, "Towards identity anonymization on graphs." In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, vol. 10, no. 2, pp. 93-106. ACM, 2008. |
[CrossRef] [SCOPUS Times Cited 464]
 B. Zhou, J. Pei, and W. Luk, "A brief survey on anonymization techniques for privacy preserving publishing of social network data," ACM Sigkdd Explorations Newsletter, vol. 10, no. 2, pp. 12- 22, 2008.
 P. Liu and X. Li, "An improved privacy preserving algorithm for publishing social network data," in Proc. 10th Int. Conf. High Perform. Comput. Commun., pp. 888895, 2013.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 6]
 D. Lusseau, "The emergent properties of a dolphin social network." Proceedings of the Royal Society of London B: Biological Sciences, vol. 270, no. 2, 2003.
[CrossRef] [Web of Science Times Cited 377]
 Tian, Yuanyuan, Richard A. Hankins, and Jignesh M. Patel. "Efficient aggregation for graph summarization." In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, vol. 37, no. 2, pp. 567-580. ACM, 2008.
[CrossRef] [SCOPUS Times Cited 279]
 Campan A., Truta T.M., "Data and Structural k-Anonymity in Social Networks." In: Bonchi F., Ferrari E., Jiang W., Malin B. (eds) Privacy, Security, and Trust in KDD. Lecture Notes in Computer Science, vol. 545, pp 33-54, . Springer, Berlin, Heidelberg, 2009.
[CrossRef] [SCOPUS Times Cited 74]
 Bonchi, F., Gionis, A. and Tassa, T. "Identity obfuscation in graphs through the information theoretic lens." Information Sciences, vol. 275, pp.232-256, 2014.
[CrossRef] [SCOPUS Times Cited 36]
 Ying, Xiaowei, and Xintao Wu. "Randomizing social networks: a spectrum preserving approach." In Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, pp. 739-750, 2008.
 L. Backstrom, C. Dwork, and J. M. Kleinberg, "Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography," Commun. ACM, vol. 54, no. 12, pp. 133141, 2011.
[CrossRef] [SCOPUS Times Cited 495]
 L. Sweeney, "Achieving k-anonymity privacy protection using generalization and suppression," Int. J. Uncertainty Fuzziness Knowl. Based Syst., vol. 10, no. 5, pp. 571588, 2002.
[CrossRef] [Web of Science Times Cited 2753] [SCOPUS Times Cited 4073]
 L. Zou, L. Chan, and M. T. Ozsu, "K-automorphism: A general framework for privacy preserving network publication," in Proc. VLDB Endowment, vol. 2, pp. 946957, 2009,
[CrossRef] [SCOPUS Times Cited 253]
 J. Medina and M. Ojeda-Aciego, "Multi-adjoint t-concept lattices." Information Sciences, vol. 180, no. 5, pp. 712725, 2010,
[CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 129]
 Tabales N., Rey J., Carmona F., Caridad Y., "Commercial properties prices appraisal: alternative approach based on neural networks.", Int. Journal of Artificial Intelligence, vol. 14, no. 1, pp. 53-70, 2016.
 O. Geman, H. Costin, "Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier," Advances in Electrical and Computer Engineering, vol.14, no.1, pp.133-138, 2014,
[CrossRef] [Full Text] [Web of Science Times Cited 18] [SCOPUS Times Cited 19]
 C. Pozna, N. Minculete, R.-E. Precup, L. T. Koczy, A. Ballagi: "Signatures: Definitions, Operators and Applications to Fuzzy Modeling", Fuzzy Sets and Systems, Vol. 201, pp. 86-104, 2012.
 Y. Wang, L.Xie, B. Zheng, and K. C. Lee, "High utility k-anonymization for social network publishing", Knowledge and Information Systems, vol. 41, no. 3, pp. 697-725, 2014.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 20]
 Fortunato, S. "Community detection in graphs." Physics reports, vol. 486, no.3-5, pp.75-174, 2010.
[CrossRef] [Web of Science Times Cited 4091] [SCOPUS Times Cited 5058]
 Shi, J. and Malik, J. "Normalized cuts and image segmentation." IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 8, pp. 888-905, 2000.
[CrossRef] [Web of Science Times Cited 6508] [SCOPUS Times Cited 9200]
 Newman, M.E. and Girvan, M. "Finding and evaluating community structure in networks." Physical review E, vol. 69, no. 2, pp. 026113, 2004.
[CrossRef] [Web of Science Times Cited 5671] [SCOPUS Times Cited 6827]
 Wong, Raymond Chi-Wing, Ada Wai-Chee Fu, Ke Wang, and Jian Pei. "Minimality attack in privacy preserving data publishing." In Proceedings of the 33rd Int. conference on Very large data bases, vol. 16, no. 4, pp. 543-554, 2007.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 72]
 Maiya, Arun S., and Tanya Y. Berger-Wolf. "Sampling community structure." In Proceedings of the 19th international conference on World wide web, pp. 701-710. ACM, 2010.
[CrossRef] [SCOPUS Times Cited 75]
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Stefan cel Mare University of Suceava, Romania
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