|4/2017 - 14|
k-Degree Anonymity Model for Social Network Data PublishingMACWAN, K. R. , PATEL, S. J.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,454 KB) | Citation | Downloads: 554 | Views: 1,736|
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 512]
 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 5] [SCOPUS Times Cited 9]
 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 450] [SCOPUS Times Cited 539]
 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 317]
 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 85]
 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 38]
 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 545]
 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 3468] [SCOPUS Times Cited 4713]
 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 285]
 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 113] [SCOPUS Times Cited 142]
 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 20] [SCOPUS Times Cited 21]
 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 17] [SCOPUS Times Cited 23]
 Fortunato, S. "Community detection in graphs." Physics reports, vol. 486, no.3-5, pp.75-174, 2010.
[CrossRef] [Web of Science Times Cited 4959] [SCOPUS Times Cited 6053]
 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 7526] [SCOPUS Times Cited 10119]
 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 14174] [SCOPUS Times Cited 7936]
 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 57] [SCOPUS Times Cited 79]
 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 93]
Web of Science® Citations for all references: 30,789 TCR
SCOPUS® Citations for all references: 31,509 TCR
Web of Science® Average Citations per reference: 1,400 ACR
SCOPUS® Average Citations per reference: 1,432 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2021-01-22 16:37 in 136 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.