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High utility K-anonymization for social network publishing

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Abstract

Privacy and utility are two main desiderata of good sensitive information publishing schemes. For publishing social networks, many existing algorithms rely on \(k\)-anonymity as a criterion to guarantee privacy protection. They reduce the utility loss by first using the degree sequence to model the structural properties of the original social network and then minimizing the changes on the degree sequence caused by the anonymization process. However, the degree sequence-based graph model is simple, and it fails to capture many important graph topological properties. Consequently, the existing anonymization algorithms that rely on this simple graph model to measure utility cannot guarantee generating anonymized social networks of high utility. In this paper, we propose novel utility measurements that are based on more complex community-based graph models. We also design a general \(k\)-anonymization framework, which can be used with various utility measurements to achieve \(k\)-anonymity with small utility loss on given social networks. Finally, we conduct extensive experimental evaluation on real datasets to evaluate the effectiveness of the new utility measurements proposed. The results demonstrate that our scheme achieves significant improvement on the utility of the anonymized social networks compared with the existing anonymization algorithms. The utility losses of many social network statistics of the anonymized social networks generated by our scheme are under 1 % in most cases.

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Notes

  1. The attacker could possess some non-structural information about the targets as well (e.g., the labels of vertices and edges), but in this paper, we only consider the structural background knowledge.

  2. Zhou et al provide the NP-hardness proof in Zhou and Pei [37] by an induction from the \(k\)-anonymity problem in relational data.

  3. We want to highlight that the additive adjustment only applies to the refining local structure information step of Algorithm 1 (i.e., lines 10–11). In other part of the algorithm (e.g., lines 5–9 where edge operations are performed to change the current graph toward the target graph), we consider both the edge addition operations and the edge deletion operations.

  4. The \(y\)-axis is in logarithm scale.

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Acknowledgments

This study was funded through a research Grant 10-C220-SMU-005 from the Office of Research, Singapore Management University.

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Authors and Affiliations

  1. School of Information Systems, Singapore Management University, Singapore, Singapore

    Yazhe Wang, Long Xie & Baihua Zheng

  2. Department of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, USA

    Ken C. K. Lee

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  1. Yazhe Wang
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  2. Long Xie
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  3. Baihua Zheng
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  4. Ken C. K. Lee
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Correspondence to Yazhe Wang.

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Wang, Y., Xie, L., Zheng, B. et al. High utility K-anonymization for social network publishing. Knowl Inf Syst 41, 697–725 (2014). https://doi.org/10.1007/s10115-013-0674-2

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  • DOI: https://doi.org/10.1007/s10115-013-0674-2

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