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With the popularity of social networks, data privacy preserving in social networks has become a hot issue among scholars. An attacker can use a variety of background knowledge to attack against privacy. Most of the present technology on anonymity weighted social network graphs can only deal with edge weight, but cannot be applied to sensitive labels. We consider a new generalization approach for sensitive...
Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals, such as diseases of patients, the credit rating of a customer, and the salary of an employee. Meanwhile, certain information is required to be published. In this paper, we consider data-publishing applications where the publisher specifies both sensitive information and shared...
K-anonymization is an important approach to protect data privacy in data publishing. When there are multiple constraints, K-anonymizing a table to satisfy all the constraints is much more complex. We propose a K-anonymization approach, FTB-Classfly, which is based on frequent tables. In stead of generalizing all values in an attribute, FTB-Classfly only generalizes partial tuples that do not satisfy...
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