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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

Read More »


    
 

  4/2017 - 14

k-Degree Anonymity Model for Social Network Data Publishing

MACWAN, K. R. See more information about MACWAN, K. R. on SCOPUS See more information about MACWAN, K. R. on IEEExplore See more information about MACWAN, K. R. on Web of Science, PATEL, S. J. See more information about PATEL, S. J. on SCOPUS See more information about PATEL, S. J. on SCOPUS See more information about PATEL, S. J. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,454 KB) | Citation | Downloads: 173 | Views: 462

Author keywords
data privacy, data processing, publishing, social network services, utility programs

References keywords
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

Abstract
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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

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[CrossRef] [SCOPUS Times Cited 412]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 65]


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[CrossRef] [SCOPUS Times Cited 35]


[8] 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.
[CrossRef]


[9] 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. 133–141, 2011.
[CrossRef] [SCOPUS Times Cited 454]


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[15] 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.

[16] 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.
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[CrossRef] [SCOPUS Times Cited 68]




References Weight

Web of Science® Citations for all references: 16,553 TCR
SCOPUS® Citations for all references: 24,135 TCR

Web of Science® Average Citations per reference: 752 ACR
SCOPUS® Average Citations per reference: 1,097 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 2018-10-17 13:29 in 146 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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