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Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing

    https://doi.org/10.1142/S0218194017500048Cited by:15 (Source: Crossref)

    With the popularity of social networks, privacy issues with regard to publishing social network data have gained intensive focus from academia. We analyzed the current privacy-preserving techniques for publishing social network data and defined a privacy-preserving model with privacy guarantee k. With our definitions, the existing privacy-preserving methods, k-anonymity and randomization can be combined together to protect data privacy. We also considered the privacy threat with label information and modify the k-anonymity technique of tabular data to protect the published data from being attacked by the combination of two types of background knowledge, the structural and label knowledge. We devised a partial k-anonymity algorithm and implemented it in Python and open source packages. We compared the algorithm with related k-anonymity and random techniques on three real-world datasets. The experimental results show that the partial k-anonymity algorithm preserves more data utilities than the k-anonymity and randomization algorithms.