research-article

Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information

Authors:

Xing XieAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 4

Article No.: 49, Pages 1 - 29

https://doi.org/10.1145/2700836

Published: 13 July 2015 Publication History

Abstract

The study of online social networks has attracted increasing interest. However, concerns are raised for the privacy risks of user data since they have been frequently shared among researchers, advertisers, and application developers. To solve this problem, a number of anonymization algorithms have been recently developed for protecting the privacy of social graphs. In this article, we proposed a graph node similarity measurement in consideration with both graph structure and descriptive information, and a deanonymization algorithm based on the measurement. Using the proposed algorithm, we evaluated the privacy risks of several typical anonymization algorithms on social graphs with thousands of nodes from Microsoft Academic Search, LiveJournal, and the Enron email dataset, and a social graph with millions of nodes from Tencent Weibo. Our results showed that the proposed algorithm was efficient and effective to deanonymize social graphs without any initial seed mappings. Based on the experiments, we also pointed out suggestions on how to better maintain the data utility while preserving privacy.

References

[1]

Réka Albert and Albert-László Barabási. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 74, 1 (Jan. 2002), 47--97.

[2]

Lars Backstrom, Cynthia Dwork, and Jon Kleinberg. 2007. Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). 181--190.

Digital Library

[3]

Albert-László Barabási, Hawoong Jeong, Zoltán Néda, Erzsébet Ravasz, András Schubert, and Tamás Vicsek. 2002. Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications 311, 3 (2002), 590--614.

[4]

Vincent D. Blondel, Anahí Gajardo, Maureen Heymans, Pierre Senellart, and Paul Van Dooren. 2004. A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM Review 46, 4 (April 2004), 647--666.

Digital Library

[5]

Phillip Bonacich. 1987. Power and centrality: A family of measures. American Journa of Sociology (1987), 1170--1182.

[6]

Francesco Bonchi, Aristides Gionis, and Tamir Tassa. 2011. Identity obfuscation in graphs through the information theoretic lens. In Proceedings of the 27th International Conference on Data Engineering (ICDE'11). 924--935.

Digital Library

[7]

Alina Campan and Traian Marius Truta. 2009. Data and structural k-anonymity in social networks. In Privacy, Security, and Trust in KDD. 33--54.

[8]

James Cheng, Ada Wai-chee Fu, and Jia Liu. 2010. K-isomorphism: Privacy preserving network publication against structural attacks. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 459--470.

Digital Library

[9]

Stephen A Cook. 1971. The complexity of theorem-proving procedures. In Proceedings of the 3rd ACM Symposium on Theory of Computing. 151--158.

Digital Library

[10]

Graham Cormode, Divesh Srivastava, Ting Yu, and Qing Zhang. 2008. Anonymizing bipartite graph data using safe groupings. Proceedings of the VLDB Endowment 1, 1 (Aug. 2008), 833--844.

Digital Library

[11]

Cynthia Dwork. 2006. Differential privacy. Automata, Languages and Programming 4052 (2006), 1--12.

Digital Library

[12]

Hao Fu, Aston Zhang, and Xing Xie. 2014. De-anonymizing social graphs via node similarity. In Proceedings of the 23rd International Conference on World Wide Web Companion (WWW'14).

Digital Library

[13]

Gabor Gyorgy Gulyas and Sandor Imre. 2014. Measuring importance of seeding for structural de-anonymization attacks in social networks. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops'14). 610--615.

[14]

Pritam Gundecha, Geoffrey Barbier, and Huan Liu. 2011. Exploiting vulnerability to secure user privacy on a social networking site. In Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining (KDD'11). 511--519.

Digital Library

[15]

Michael Hay, Gerome Miklau, David Jensen, Don Towsley, and Philipp Weis. 2008. Resisting structural re-identification in anonymized social networks. Proceedings of the VLDB Endowment 1, 1 (2008), 102--114.

Digital Library

[16]

Michael Hay, Vibhor Rastogi, Gerome Miklau, and Dan Suciu. 2010. Boosting the accuracy of differentially private histograms through consistency. Proceedings of the VLDB Endowment 3, 1 (2010).

Digital Library

[17]

Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong, and Christos Faloutsos. 2011. It's who you know: Graph mining using recursive structural features. In Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining (KDD'11). 663--671.

Digital Library

[18]

Glen Jeh and Jennifer Widom. 2002. SimRank: A measure of structural-context similarity. In Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining (KDD'11). 538--543.

Digital Library

[19]

Vishesh Karwa, Sofya Raskhodnikova, Adam Smith, and Grigory Yaroslavtsev. 2011. Private analysis of graph structure. Proceedings of the VLDB Endowment 4, 11 (2011), 1146--1157.

Digital Library

[20]

Mohammed Korayem and David J Crandall. 2013. De-anonymizing users across heterogeneous social computing platforms. In the 7th International AAAI Conference on Weblogs and Social Media (ICWSM'13).

[21]

Harold W. Kuhn. 1955. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 1--2 (1955), 83--97.

[22]

Jure Leskovec and Christos Faloutsos. 2006. Sampling from large graphs. In Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (KDD'06). 631--636.

Digital Library

[23]

David Liben-Nowell and Jon Kleinberg. 2003. The link prediction problem for social networks. In Proceedings of the 12th International Conference on Information and Knowledge Management (CIKM'03). 556--559.

Digital Library

[24]

Kun Liu and Evimaria Terzi. 2008. Towards identity anonymization on graphs. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 93--106.

Digital Library

[25]

Sergey Melnik, Hector Garcia-Molina, and Erhard Rahm. 2002. Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In Proceedings of the 18th International Conference on Data Engineering (ICDE'02). 117--128.

Digital Library

[26]

Alan Mislove, Bimal Viswanath, Krishna P. Gummadi, and Peter Druschel. 2010. You are who you know: Inferring user profiles in online social networks. In Proceedings of the 3rd International Conference on Web Search and Data Mining (WSDM'10). 251--260.

Digital Library

[27]

Prateek Mittal, Charalampos Papamanthou, and Dawn Song. 2013. Preserving link privacy in social network based systems. In Network and Distributed System Security Symposium (NDSS'13).

[28]

Arvind Narayanan, Elaine Shi, and Benjamin I. P. Rubinstein. 2011. Link prediction by de-anonymization: How we won the kaggle social network challenge. In Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN). 1825--1834.

[29]

Arvind Narayanan and Vitaly Shmatikov. 2009. De-anonymizing social networks. In Proceedings of the 2009 IEEE Symposium on Security and Privacy. 173--187.

Digital Library

[30]

Pedram Pedarsani and Matthias Grossglauser. 2011. On the privacy of anonymized networks. In Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining (KDD'11). 1235--1243.

Digital Library

[31]

Alessandra Sala, Xiaohan Zhao, Christo Wilson, Haitao Zheng, and Ben Y. Zhao. 2011. Sharing graphs using differentially private graph models. In Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference (IMC'11). 81--98.

Digital Library

[32]

Chih-Hua Tai, Philip S. Yu, De-Nian Yang, and Ming-Syan Chen. 2011. Privacy-preserving social network publication against friendship attacks. In Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining (KDD'11). 1262--1270.

Digital Library

[33]

Tamir Tassa and Dror J. Cohen. 2013. Anonymization of centralized and distributed social networks by sequential clustering. IEEE Transactions on Knowledge and Data Engineering (TKDE) 25, 2 (Feb. 2013), 311--324.

Digital Library

[34]

Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of'small-world' networks. Nature 393, 6684 (June 1998), 440--442.

[35]

Leting Wu, Xiaowei Ying, and Xintao Wu. 2010b. Reconstruction from randomized graph via low rank approximation. In Proceedings of the 2010 SIAM International Conference on Data Mining (SDM'10). 60--71.

[36]

Wentao Wu, Yanghua Xiao, Wei Wang, Zhenying He, and Zhihui Wang. 2010a. K-symmetry model for identity anonymization in social networks. In Proceedings of the 13th International Conference on Extending Database Technology (EDBT'10). 111--122.

Digital Library

[37]

Xintao Wu, Xiaowei Ying, Kun Liu, and Lei Chen. 2010c. A survey of privacy-preservation of graphs and social networks. Managing and Mining Graph Data (2010), 421--453.

[38]

Jaewon Yang and Jure Leskovec. 2012. Defining and evaluating network communities based on ground-truth. In Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM'10). 745--754.

Digital Library

[39]

Lyudmila Yartseva and Matthias Grossglauser. 2013. On the performance of percolation graph matching. In Proceedings of the 1st ACM Conference on Online Social Networks (COSN'13). 119--130.

Digital Library

[40]

Xiaowei Ying and Xintao Wu. 2008. Randomizing social networks: A spectrum preserving approach. In Proceedings of the 2008 SIAM International Conference on Data Mining (SDM). 739--750.

[41]

Xiaowei Ying and Xintao Wu. 2009. Graph generation with prescribed feature constraints. In Proceedings of the 2009 SIAM International Conference on Data Mining (SDM'09), Vol. 9. 966--977.

[42]

Xiaowei Ying and Xintao Wu. 2011. On link privacy in randomizing social networks. Knowledge and Information Systems 28, 3 (2011), 645--663.

Digital Library

[43]

Aston Zhang, Xing Xie, Kevin Chen-Chuan, Carl A. Gunter, Jiawei Han, and XiaoFeng Wang. 2014. Privacy risk in anonymized heterogeneous information networks. In Proceedings of the 17th International Conference on Extending Database Technology (EDBT'14).

[44]

Elena Zheleva and Lise Getoor. 2008. Preserving the privacy of sensitive relationships in graph data. In Privacy, Security, and Trust in KDD. 153--171.

Digital Library

[45]

Bin Zhou and Jian Pei. 2008. Preserving privacy in social networks against neighborhood attacks. In Proceedings of the 24th International Conference on Data Engineering (ICDE'08). 506--515.

Digital Library

[46]

Lei Zou, Lei Chen, and M. Tamer Özsu. 2009. K-automorphism: A general framework for privacy preserving network publication. Proceedings of the VLDB Endowment 2, 1 (2009), 946--957.

Digital Library

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Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 4

Regular Papers and Special Section on Intelligent Healthcare Informatics

August 2015

419 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2801030

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2015

Accepted: 01 December 2014

Received: 01 October 2014

Published in TIST Volume 6, Issue 4

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Cited By

Tang RYong ZJiang SChen XLiu YZhang YSun GWang W(2025)Network alignmentPhysics Reports10.1016/j.physrep.2024.11.0061107(1-45)Online publication date: Mar-2025
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Nguyen TNguyen TNguyen TYin HNguyen TJo JNguyen Q(2023)Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph MiningACM Transactions on Intelligent Systems and Technology10.1145/363063515:1(1-26)Online publication date: 19-Dec-2023
https://dl.acm.org/doi/10.1145/3630635
Wang LZhang YHu K(2022)FEUI: Fusion Embedding for User Identification across social networksApplied Intelligence10.1007/s10489-021-02716-552:7(8209-8225)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s10489-021-02716-5
Miao XZhang WJiang YFu FShao YChen LTao YCao GCui B(2022)P$$^2$$CG: a privacy preserving collaborative graph neural network training frameworkThe VLDB Journal10.1007/s00778-022-00768-832:4(717-736)Online publication date: 4-Nov-2022
https://doi.org/10.1007/s00778-022-00768-8
Su JCao YChen YLiu YSong J(2021)Privacy protection of medical data in social networkBMC Medical Informatics and Decision Making10.1186/s12911-021-01645-021:S1Online publication date: 18-Oct-2021
https://doi.org/10.1186/s12911-021-01645-0
Ma CChang CMa THuang JNiu Z(2021)User Identity Matching for Multisource Location Data2021 IEEE 21st International Conference on Communication Technology (ICCT)10.1109/ICCT52962.2021.9657893(686-694)Online publication date: 13-Oct-2021
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Xian XWu TLiu YWang WWang CXu GXiao Y(2021)Towards link inference attack against network structure perturbationKnowledge-Based Systems10.1016/j.knosys.2020.106674218:COnline publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1016/j.knosys.2020.106674
Celosia GCunche M(2020)Discontinued Privacy: Personal Data Leaks in Apple Bluetooth-Low-Energy Continuity ProtocolsProceedings on Privacy Enhancing Technologies10.2478/popets-2020-00032020:1(26-46)Online publication date: 7-Jan-2020
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Li NMurugesan RLi LZheng H(2020)IDEAL: An Interactive De-Anonymization Learning System2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC48688.2020.0-210(449-454)Online publication date: Jul-2020
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Xian XWu TQiao SWang WLiu YHan N(2020)Multi-View Low-Rank Coding-Based Network Data De-AnonymizationIEEE Access10.1109/ACCESS.2020.29955688(94575-94593)Online publication date: 2020
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