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

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.