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International Journal of Information Security

, Volume 18, Issue 2, pp 125–139 | Cite as

Hiding information against structural re-identification

  • Gábor György Gulyás Email author
  • Sándor Imre
Regular Contribution
  • 120 Downloads

Abstract

Connections between users of social networking services pose a significant privacy threat. Recently, several social network de-anonymization attacks have been proposed that can efficiently re-identify users at large scale, solely considering the graph structure. In this paper, we consider these privacy threats and analyze de-anonymization attacks at the model level against a user-controlled privacy-enhancing technique called identity separation. The latter allows creating seemingly unrelated identities in parallel, even without the consent of the service provider or other users. It has been shown that identity separation can be used efficiently against re-identification attacks if user cooperate with each other. However, while participation would be crucial, this cannot be granted in a real-life scenario. Therefore, we introduce the y-identity model, in which the user creates multiple separated identities and assigns the sensitive attribute to one of them according to a given strategy. For this, we propose a strategy to be used in real-life situations and formally prove that there is a higher bound for the expected privacy loss which is sufficiently low.

Keywords

Anonymity Privacy Social networks Re-identification Identity separation 

Notes

Acknowledgements

The authors would like to thank Levente Buttyán, István Vajda and Benedek Simon for the fruitful conversations. We are very grateful for Márk Félegyházi and Tamás Holczer for reviewing draft versions of this paper and for engaging us in meaningful discussions. We would like to also thank the useful comments and suggestions of Gergely Biczók and also for reviewing the y-identity model in details. The authors are thankful for the comments and suggestions provided by the members of the CrySyS reading group.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Privatics TeamINRIAMontbonnotFrance
  2. 2.Mobile Communications and Quantum Technologies Laboratory (MCL), Department of Networked Systems and ServicesBMEBudapestHungary

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