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- W2617149097 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." @default.
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- W2617149097 date "2017-01-01" @default.
- W2617149097 modified "2023-09-27" @default.
- W2617149097 title "Preserving Privacy in Social Networks Against Label Pair Attacks" @default.
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- W2617149097 doi "https://doi.org/10.1007/978-3-319-60033-8_34" @default.
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