{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:23:56Z","timestamp":1765887836572},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2017,2]]},"abstract":"<jats:p> 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 [Formula: see text]. With our definitions, the existing privacy-preserving methods, [Formula: see text]-anonymity and randomization can be combined together to protect data privacy. We also considered the privacy threat with label information and modify the [Formula: see text]-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 [Formula: see text]-anonymity algorithm and implemented it in Python and open source packages. We compared the algorithm with related [Formula: see text]-anonymity and random techniques on three real-world datasets. The experimental results show that the partial [Formula: see text]-anonymity algorithm preserves more data utilities than the [Formula: see text]-anonymity and randomization algorithms. <\/jats:p>","DOI":"10.1142\/s0218194017500048","type":"journal-article","created":{"date-parts":[[2016,11,21]],"date-time":"2016-11-21T04:14:51Z","timestamp":1479701691000},"page":"71-90","source":"Crossref","is-referenced-by-count":17,"title":["Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing"],"prefix":"10.1142","volume":"27","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Haidian, Beijing 100191, P. R. China"},{"name":"Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi 541004, P. R. China"}]},{"given":"Yan","family":"Bai","sequence":"additional","affiliation":[{"name":"Institute of Technology, University of Washington Tacoma, Tacoma, WA 98402, USA"}]},{"given":"Lie","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi 541004, P. R. China"}]},{"given":"Xianxian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Haidian, Beijing 100191, P. R. China"},{"name":"Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi 541004, P. R. 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