Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Anonymization and De-anonymization of Social Network Data

  • Sean Chester
  • Bruce M. Kapron
  • Gautam SrivastavaEmail author
  • Venkatesh Srinivasan
  • Alex Thomo
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_22




Adversarial model

Formal description of the unique characteristics of a particular adversary


Somebody who attempts to reveal sensitive, private information

Attribute disclosure

A privacy breach wherein some descriptive attribute of somebody is revealed

Identity disclosure

A privacy breach in which a presumably anonymous person is in fact identifiable


The particular social network member against whom an adversary is trying to breach privacy


As social networks grow and become increasingly pervasive, so too do the opportunities to analyze the data that arises from them. Social network data can be released for public research that can lead to breakthroughs in fields as diverse as marketing and health care. But with the release of data come questions of privacy. Is there any information that members of the social network would...

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  1. Aggarwal G, Feder T, Kenthapadi K, Motwani R, Panigrahy R, Thomas D, Zhu A (2005) Anonymizing tables. In: ICDT 2005. Edinburgh, pp 246–258CrossRefGoogle Scholar
  2. Backstrom L et al (2007) Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW 2007. Banff, Alberta, pp 181–190Google Scholar
  3. Bonizzoni P, Vedova GD, Dondi R (2009) The -anonymity problem is hard. In: FCT 2009, pp 26–37CrossRefGoogle Scholar
  4. Chester S, Kapron BM, Srivastava G, Venkatesh S (2013) Complexity of social network anonymization. Soc Netw Anal Min 3(2):151–166CrossRefGoogle Scholar
  5. Chester S, Kapron BM, Srivastava G, Venkatesh S, Thomo A (2014) Anonymization and de-anonymization of social network data. In: Encyclopedia of social network analysis and mining. Springer, pp 48–56Google Scholar
  6. Colorni A, Dorigo M, Maniezzo V (1992) An investigation of some properties of an “ant algorithm”. In: Parallel problem solving from nature 2, PPSN-II, Brussels, 28–30 Sept 1992. Brussels, pp 515–526Google Scholar
  7. Cordón O, de Viana IF, Herrera F (2002) Analysis of the best-worst ant system and its variants on the QAP. In: Ant algorithms, third international workshop, ANTS 2002, Brussels, 12–14 Sept 2002, Proceedings, pp 228–234zbMATHCrossRefGoogle Scholar
  8. Douceur JR (2002) The sybil attack. In: Revised papers from the first international workshop on peer-to-peer systems, IPTPS ’01. Springer, London, pp 251–260CrossRefGoogle Scholar
  9. Hartl RF, Bullnheimer B, Strauss C (1999) An improved ant system algorithm for the vehicle routing problem. Ann Oper Res 89:319–328MathSciNetzbMATHCrossRefGoogle Scholar
  10. Hay M et al (2008) Resisting structural re-identification in anonymized social networks. PVLDB 1(1):102–114Google Scholar
  11. Ji S, Li W, Srivatsa M, Beyah R (2014a) Structural data de-anonymization: quantification, practice, and implications. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. ACM. Scottsdale AZ, pp 1040–1053Google Scholar
  12. Ji S, Li W, Srivatsa M, He JS, Beyah R (2014b) Structure based data de-anonymization of social networks and mobility traces. In: International conference on information security. Springer, Switzerland, pp 237–254Google Scholar
  13. Kapron BM, Srivastava G, Venkatesh S (2011) Social network anonymization via edge addition. In: Proceedings of the ASONAM 2011. Kaoshiung, pp 155–162Google Scholar
  14. Li C, Amagasa T, Kitagawa H, Srivastava G (2014) Label-bag based graph anonymization via edge addition. In: International C* conference on computer science & software engineering, C3S2E ’14, Montreal, 03–05 Aug 2014, pp 1:1–1:9Google Scholar
  15. Liu K, Terzi E (2008) Towards identity anonymization on graphs. In: SIGMOD 2008. Vancouver, pp 93–106Google Scholar
  16. Mittal P, Papamanthou C, Song D (2012) Preserving link privacy in social network based systems. arXiv preprint arXiv:1208.6189Google Scholar
  17. Narayanan A, Shmatikov V (2006) How to break anonymity of the netflix prize dataset. CoRR, abs/cs/0610105Google Scholar
  18. Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: Security and privacy, 2008. SP 2008. IEEE symposium on. IEEE. Oakland, CA, pp 111–125Google Scholar
  19. Narayanan A, Shi E, Rubinstein BI (2011) Link prediction by de-anonymization: how we won the kaggle social network challenge. In: Neural Networks (IJCNN), The 2011 international joint conference on. IEEE. San Jose CA, pp 1825–1834Google Scholar
  20. Nilizadeh S, Kapadia A, Ahn Y-Y (2014) Community-enhanced de-anonymization of online social networks. In: Proceedings of the 2014 acm sigsac conference on computer and communications security. ACM. Scottsdale AZ, pp 537–548Google Scholar
  21. Pedarsani P, Figueiredo DR, Grossglauser M (2013) A bayesian method for matching two similar graphs without seeds. In: Communication, control, and computing (Allerton), 2013 51st annual Allerton conference on. IEEE. pp 1598–1607Google Scholar
  22. Proserpio D, Goldberg S, McSherry F (2012) A workflow for differentially-private graph synthesis. In: Proceedings of the 2012 ACM workshop on workshop on online social networks. ACM, Helsinki, pp 13–18Google Scholar
  23. Proserpio D, Goldberg S, McSherry F (2014) Calibrating data to sensitivity in private data analysis: a platform for differentially-private analysis of weighted datasets. Proc VLDB Endow 7(8):637–648CrossRefGoogle Scholar
  24. Qian J, Li X-Y, Zhang C, Chen L, Jung T, Han J (2017) Social network de-anonymization and privacy inference with knowledge graph model. IEEE Trans Dependable Secure Comput, issue 99Google Scholar
  25. Sala A, Zhao X, Wilson C, Zheng H, Zhao BY (2011) Sharing graphs using differentially private graph models. In: Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference. ACM. Berlin, pp 81–98Google Scholar
  26. Srivastava G, Citulsky E, Tilbury K, Abdelbar A, Amagasa T (2016) The effects of ant colony optimization on graph anonymization. GSTF J Comput (JoC) 5(1):91Google Scholar
  27. Srivastava G, Shumay M, Citulsky E (2017) Social network anonymity using ant colony systems. In: International conference on Computer Games, Multimedia & Allied Technology (CGAT). Proceedings, Global Science and Technology Forum. Singapore, p 64Google Scholar
  28. Sweeney L (1997) Datafly: a system for providing anonymity in medical data. In: Database securty XI: status and prospects, IFIP TC11 WG11.3 eleventh international conference on database security, Lake Tahoe, 10–13 Aug 1997, pp 356–381Google Scholar
  29. Thompson B, Yao D (2009) The union-split algorithm and cluster-based anonymization of social networks. In: ASIACCS 2009. Sydeney, pp 218–227Google Scholar
  30. Tripathy BK, Panda GK (2010) A new approach to manage security against neighborhood attacks in social networks. In: ASONAM. Odense, pp 264–269Google Scholar
  31. Wu W, Xiao Y, Wang W, He Z, Wang Z (2010) k-symmetry model for identity anonymization in social networks. In: EDBT, pp 111–122Google Scholar
  32. Ying X, Wu X (2008) Randomizing social networks: a spectrum preserving approach. In: Proceedings of the 2008 SIAM international conference on data mining. SIAM. Atlanta, GA, pp 739–750CrossRefGoogle Scholar
  33. Yu H, Gibbons PB, Kaminsky M, Xiao F (2008) Sybillimit: a near-optimal social network defense against sybil attacks. In: Security and Privacy. SP 2008. IEEE symposium on. IEEE. Oakland, CA, pp 3–17Google Scholar
  34. Yuan M, Chen L, Yu PS (2010) Personalized privacy protection in social networks. Proc VLDB Endow 4(2):141–150CrossRefGoogle Scholar
  35. Zhang S, Liu Q, Lin Y (2017) Anonykmizing popularity in online social networks with full utility. Futur Gener Comput Syst 72:227–238CrossRefGoogle Scholar
  36. Zhou B et al (2008) Preserving privacy in social networks against neighborhood attacks. In: ICDE 2008. Cancun, pp 506–515Google Scholar

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sean Chester
    • 1
  • Bruce M. Kapron
    • 2
  • Gautam Srivastava
    • 3
    Email author
  • Venkatesh Srinivasan
    • 2
  • Alex Thomo
    • 2
  1. 1.Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Department of Computer ScienceUniversity of VictoriaVictoriaCanada
  3. 3.Department of Math and Computer ScienceBrandon UniversityBrandonCanada

Section editors and affiliations

  • Fabrizio Silvestri
    • 1
  • Andrea Tagarelli
    • 2
  1. 1.Yahoo IncLondonUK
  2. 2.University of CalabriaArcavacata di RendeItaly