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K-anonymity for social networks containing rich structural and textual information

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Abstract

When social networks are released for analysis, individuals’ sensitive information (e.g., node identities) in the network may be exposed. To avoid unwanted information exposure, social networks need to be anonymized before they are published. In the literature, many approaches exist to anonymize social networks to prevent attacks by adversaries that know the network structures such as node degrees and neighbors. However, these techniques cannot prevent the leakage of valuable identification information during social network analysis if the social network graphs contain both structural and textual information. In this paper, we study the problem of anonymizing social networks to prevent individual identifications which use both structural (node degrees) and textual (edge labels) information in graphs. We formally define the problem as Structure and Text aware \(K\)-anonymity of social networks (STK-Anonymity). In an STK-anonymized network, each individual is \(ST\)-equivalent to at least \(K-1\) other nodes. The major challenge in achieving STK-Anonymity comes from the correlation of edge labels, which causes the propagation of edge anonymization. It has been shown that it is intractable to optimally \(K\)-anonymizing the label sequences of edge-labeled graphs. To address the challenge, we present a two-phase approach which consists of two heuristics in the first phase to process partial graph structures (node degrees in particular) and a set-enumeration tree-based approach in the second phase to anonymize edge labels. Results from extensive experiments on both real and synthetic datasets are presented to show the effectiveness and efficiency of our approaches.

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Notes

  1. A table with missing values is an exception.

  2. Multiple edges are combined to one topological edge with multiple edge labels.

  3. In anonymizing microdata, the cell-based anonymization strategy (Meyerson and Williams 2004; Park and Shim 2007) uses a many to many mapping to perform the anonymization, i.e., the same label \(el\) may be generalized to multiple other labels. Such \(K\)-anonymization problem has shown to be NP-Hard even when the attribute values are ternary (Aggarwal et al. 2005). In this work, we do not consider the anonymization solutions with such fine granularities.

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Authors and Affiliations

  1. Computer Science, New Mexico State University, Las Cruces, NM, 88011, USA

    Yifan Hao, Huiping Cao, Chuan Hu, Kabi Bhattarai & Satyajayant Misra

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  1. Yifan Hao
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  2. Huiping Cao
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  3. Chuan Hu
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  4. Kabi Bhattarai
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  5. Satyajayant Misra
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Corresponding author

Correspondence to Huiping Cao.

Appendix

Appendix

This section contains more figures with the utility measures for datasets with different size: Fig. 23 (DBLP 1000), Fig. 24 (DBLP 2000), Fig. 25 (DBLP 4000), Fig. 26 (DBLP8000), Fig. 27 (DBLP 32000), Fig. 28 (Synthetic N1000), Fig. 29 (Synthetic N5000), Fig. 30 (Synthetic N10000, E20000), Fig. 31 (Synthetic N10000, E40000), Fig. 32 (Synthetic N10000, E50000), Fig. 33 (Synthetic N20000, E60000). The trend that we observed from these figures is the same to that in Sect. 6.1.2.

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Utility measures: DBLP1000

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Utility measures: DBLP2000

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Utility measures: DBLP4000

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Utility measures: DBLP8000

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Utility measures: DBLP32000

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Fig. 28
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Utility measures: Synthetic N1000

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Utility measures: Synthetic N5000

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Utility measures: Synthetic N10000 E20000

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Fig. 31
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Utility measures: Synthetic N10000 E40000

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Utility measures: Synthetic N10000 E50000

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Fig. 33
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Utility measures: Synthetic N20000

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Hao, Y., Cao, H., Hu, C. et al. K-anonymity for social networks containing rich structural and textual information. Soc. Netw. Anal. Min. 4, 223 (2014). https://doi.org/10.1007/s13278-014-0223-3

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