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| An Algorithm of K-anonymity for Data Releasing Based on Fine-grained Generalization |
| Yong Xu, Xiaolin Qin, Zhongxue Yang, Yitao Yang, Can Huang |
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Abstract In recent years, publishing data about individuals without revealing
their identity information has become an active issue, and
k-anonymity-based models are effective techniques that prevent
linking attack. Existing k-anonymity algorithms did not cope with
information loss in released data set well. The paper proposes an
improved algorithm FK-anonymity. Taking data distribution into
account, FK-anonymity revises the generalization hierarchy according
to application domain, and then generalizes original table on
modified generalization hierarchy structure which increases data
quality of released table. Theoretical analysis and experimental
results testify the scheme is effective and can preserve privacy of
the sensitive data well meanwhile maintain better data utility.
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