Roland Assam
Marwan Hassani
Thomas Seidl
ABSTRACT
Networks entail vulnerable and sensitive information that pose serious privacy threats. In this paper, we introduce, k-core attack, a new attack model which stems from the k-core decomposition principle. K-core attack undermines the privacy of some state-of-the-art techniques. We propose a novel structural anonymization technique called (k, δ)-Core Anonymity, which harnesses the k-core attack and structurally anonymizes small and large networks. In addition, although real-world social networks are massive in nature, most existing works focus on the anonymization of networks with less than one hundred thousand nodes. (k, δ)-Core Anonymity is tailored for massive networks. To the best of our knowledge, this is the first technique that provides empirical studies on structural network anonymization for massive networks. Using three real and two synthetic datasets, we demonstrate the effectiveness of our technique on small and large networks with up to 1.7 million nodes and 17.8 million edges. Our experiments reveal that our approach outperforms a state-of-the-art work in several aspects.
- Stanford Large Network Dataset Collection. http://snap.stanford.edu/data/index.html, 2013.Google Scholar
- R. Albert and A.-L. Barabási. Statistical mechanics of complex networks. Rev. Mod. Phys., 2002.Google ScholarCross Ref
- J. I. Alvarez-Hamelin, L. Dall'Asta, A. Barrat, and A. Vespignani. k-core decomposition: a tool for the visualization of large scale networks. CoRR' 05, 2005.Google Scholar
- L. Backstrom, C. Dwork, and J. M. Kleinberg. Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography. 2007.Google Scholar
- J. Cheng, A. W. Fu, and J. Liu. K-isomorphism: privacy preserving network publication against structural attacks. In SIGMOD '10. Google ScholarDigital Library
- J. Cheng, Y. Ke, S. Chu, and M. Ozsu. Efficient core decomposition in massive networks. In ICDE' 11. Google ScholarDigital Library
- S. Chester, B. M. Kapron, G. Ramesh, G. Srivastava, A. Thomo, and S. Venkatesh. k-anonymization of social networks by vertex addition. In ADBIS' 11.Google Scholar
- G. Cormode, D. Srivastava, T. Yu, and Q. Zhang. Anonymizing bipartite graph data using safe groupings. Proc. VLDB Endow., 1(1):833--844, 2008. Google ScholarDigital Library
- M. Hay, G. Miklau, D. Jensen, D. Towsley, and P. Weis. Resisting structural re-identification in anonymized social networks. VLDB '08. Google ScholarDigital Library
- V. Karwa, S. Raskhodnikova, A. Smith, and G. Yaroslavtsev. Private analysis of graph structure. PVLDB, 4(11):1146--1157, 2011.Google ScholarDigital Library
- K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIGMOD '08, pages 93--106, 2008. Google ScholarDigital Library
- L. Liu, J. Wang, J. Liu, and J. Zhang. Privacy preservation in social networks with sensitive edge weights. In SDM, pages 954--965, 2009.Google ScholarCross Ref
- A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. L-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data, 2007. Google ScholarDigital Library
- A. Sala, X. Zhao, C. Wilson, H. Zheng, and B. Y. Zhao. Sharing graphs using differentially private graph models. In IMC '11, pages 81--98, 2011. Google ScholarDigital Library
- S. B. Seidman. Network structure and minimum degree. Social Networks, 1983.Google Scholar
- C.-H. Tai, P. S. Yu, D.-N. Yang, and M.-S. Chen. Privacy-preserving social network publication against friendship attacks. In KDD '11. Google ScholarDigital Library
- C.-H. Tai, P. S. Yu, D.-N. Yang, and M.-S. Chen. Structural diversity for privacy in publishing social networks. In SDM' 11, pages 35--46.Google Scholar
- A. L. Traud, P. J. Mucha, M. A. Porter, and M. A. Porter. Social structure of facebook networks. 2011.Google Scholar
- L. Wu, X. Ying, and X. Wu. Reconstruction from randomized graph via low rank approximation. In SDM, pages 60--71, 2010.Google ScholarCross Ref
- M. Xue, P. Karras, R. Chedy, P. Kalnis, and H. K. Pung. Delineating social network data anonymization via random edge perturbation. In CIKM '12. Google ScholarDigital Library
- X. Ying and X. Wu. Randomizing social networks: a spectrum preserving approach. In SDM' 08.Google Scholar
- M. Yuan, L. Chen, and P. S. Yu. Personalized privacy protection in social networks. Proc. VLDB '10. Google ScholarDigital Library
- B. Zhou and J. Pei. Preserving privacy in social networks against neighborhood attacks. In ICDE' 08. Google ScholarDigital Library
- L. Zou, L. Chen, and M. T. Özsu. K-Automorphism: A general framework for privacy preserving network publication. In VLDB '09, 2009. Google ScholarDigital Library
Index Terms
- (k, d)-core anonymity: structural anonymization of massive networks
Recommendations
k-anonymity: a model for protecting privacy
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version ...
Improved yoking proof protocols for preserving anonymity
In emerging RFID applications, the yoking proof provides a method not only to ensure the physical proximity of multiple objects but also to verify that a pair of RFID tags has been scanned simultaneously by a reader. Previous studies have focused on ...
Anonymity preserving framework for location-based information services
MEDES '10: Proceedings of the International Conference on Management of Emergent Digital EcoSystemsRecently, location based services (LBS) have become more important in today technology advancements. Privacy issue in LBS is one of the most important concerns. In this paper, we have proposed an anonymity preserving framework which can provide a user ...
Comments