Abstract
With the popularity of social networks, publishing social network data is necessary for research purposes, which causes privacy leakage undoubtedly. Therefore, many methods are proposed to deal with different attack models. This paper focuses on a novel privacy attack model and refers it as a label pair attack. In the label pair attacks, the adversary can re-identify a pair of friends by using the labels of two vertices connected by an edge. We present a new anonymity concept, called Label Pair k 2-anonymity which ensures that there exists at least k – 1 other vertices such that each of the k – 1 vertices also has an incident edge of the same label pair and reduces the probability of a vertex being re-identified to less than 1/k. The experimental results demonstrate that the approach can preserve the privacy and utility of social networks effectively.
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Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant 61572459, 61672180 and 61602129. The paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.
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Liu, C., Yin, D., Li, H., Wang, W., Yang, W. (2017). Preserving Privacy in Social Networks Against Label Pair Attacks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_34
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DOI: https://doi.org/10.1007/978-3-319-60033-8_34
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