research-article

Published Weighted Social Networks Privacy Preservation Based on Community Division

Authors:

Xueqin Zhang,

Qianru Zhou,

Chunhua GuAuthors Info & Claims

ICCNS '17: Proceedings of the 2017 7th International Conference on Communication and Network Security

Pages 86 - 90

https://doi.org/10.1145/3163058.3163074

Published: 24 November 2017 Publication History

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Abstract

Social network not only contains privacy information but also contains large valuable data for researching. It is very critical to balance the utility of data and the power of privacy preservation for published network. This paper proposes privacy preservation based on community division algorithm to preserve the privacy of published weighted social network. The algorithm firstly changes the directed graph into undirected graph whose weight is replaced by relationship strength and takes the node similarity into consideration while dividing communities, then perturb randomly in local structures to generate the published graph. NMI is used to verify the accuracy of community division, the results of three datasets prove that the algorithm can protect the privacy and guarantee the data utility of the graph structure at the same time.

References

[1]

Ying X, Pan K, Wu X, Guo L (2009) Comparisons of randomization and k-degree anonymization schemes for privacy preserving social network publishing. In: SNA-KDD '09: proceedings of the 3rd SIGKDD workshop on social network mining and analysis

Digital Library

Google Scholar

[2]

Liu L, Wang J, Liu J, et al. Privacy preserving in social networks against sensitive edge disclosure{R}. Technical Report Technical Report CMIDA-HiPSCCS 006-08, Department of Computer Science, University of Kentucky, KY, 2008.

Google Scholar

[3]

Ying X, Wu X. Randomizing social networks: a spectrum preserving approach{C}//Proceedings of the 2008 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2008: 739--750.

Google Scholar

[4]

X. Ying and X. Wu. Graph generation with prescribed feature constraints. In Proc. of the 9th SIAM Conference on Data Mining, 2009.

Crossref

Google Scholar

[5]

S. Hanhijarvi, G. C. Garriga, and K. Puolamaki. Randomization techniques for graphs. In Proc. of the 9th SIAM Conference on Data Mining, 2009.

Crossref

Google Scholar

[6]

Zhang P, Yue K, Li J, et al. Detecting community structures in microblogs from behavioral interactions{C}//Asia-Pacific Web Conference. Springer Berlin Heidelberg, 2013: 734--745.

Google Scholar

[7]

Alfalahi K, Atif Y, Harous S. Community detection in social networks through similarity virtual networks{C}// Ieee/acm International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2013:1116--1123.

Digital Library

Google Scholar

[8]

LIANG Zongwen, YANG Fan, LI Jianping. Community structure detection based on node similarity in complex networks {J}. Journal of Computer Applications, 2015, 35(5):1213--1217.

Google Scholar

[9]

Newman M E J. Fast algorithm for detecting community structure in networks{J}. Physical review E. 2004, 69 (6): 066133.

Google Scholar

[10]

Lancichinetti A, Fortunato S, Radicchi F. New benchmark in community detection{J}. Physical Review E, 2008, 78(4):561--570.

Crossref

Google Scholar

[11]

Zachary W W. An Information Flow Model for Conflict and Fission in Small Groups{J}. Journal of Anthropological Research, 1977, 33(Volume 33, Number 4):452--473.

Crossref

Google Scholar

[12]

D. J. Watts and S. H. Strogatz, "Collective dynamics of 'small-world'networks", Nature 393, 440--442 (1998).

Crossref

Google Scholar

[13]

D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993).

Crossref

Google Scholar

Cited By

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Li XDong SLi S(2023)A differentially private non-overlapping community detection method based on improved Louvain algorithm2023 6th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT60026.2023.00010(7-12)Online publication date: 28-Jul-2023
https://doi.org/10.1109/DSIT60026.2023.00010
Chen LHan KXiu QGao D(2022)Graph clustering under weight-differential privacy2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00225(1457-1464)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00225

Index Terms

Published Weighted Social Networks Privacy Preservation Based on Community Division
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

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Published In

ICCNS '17: Proceedings of the 2017 7th International Conference on Communication and Network Security

November 2017

125 pages

ISBN:9781450353496

DOI:10.1145/3163058

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 November 2017

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ICCNS 2017

ICCNS 2017: 2017 the 7th International Conference on Communication and Network Security

November 24 - 26, 2017

Tokyo, Japan

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Li XDong SLi S(2023)A differentially private non-overlapping community detection method based on improved Louvain algorithm2023 6th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT60026.2023.00010(7-12)Online publication date: 28-Jul-2023
https://doi.org/10.1109/DSIT60026.2023.00010
Chen LHan KXiu QGao D(2022)Graph clustering under weight-differential privacy2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00225(1457-1464)Online publication date: Dec-2022
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00225

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Structural Diversity for Resisting Community Identification in Published Social Networks

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