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dc.contributor.authorJana, Medková-
dc.contributor.authorJosef, Hynek-
dc.date.accessioned2023-04-10T01:48:34Z-
dc.date.available2023-04-10T01:48:34Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s13278-023-01064-1-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7697-
dc.descriptionCC BYvi
dc.description.abstractOnline social network datasets contain a large amount of various information about their users. Preserving users’ privacy while publishing or sharing datasets with third parties has become a challenging problem. The k-automorphism is the anonymization method that protects the social network dataset against any passive structural attack. It provides a higher level of protection than other k-anonymity methods, including k-degree or k-neighborhood techniques. In this paper, we propose a hybrid algorithm that effectively modifies the social network to the k-automorphism one.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjecthybrid algorithmvi
dc.subjectk-automorphism anonymizationvi
dc.titleHAkAu: hybrid algorithm for effective k-automorphism anonymization of social networksvi
dc.typeBookvi
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