Effectively combing both global and local recoding, we then propose a hybrid algorithm for utility based k-anonymization. The algorithm greedily partitions ...
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k-anonymity is a well-researched mechanism for protecting private information released in Web. It requires that each tuple of a public released table must ...
Utility-based anonymization aims to address the utility of either an entire dataset or for specific attributes in the dataset. The concept of this type of ...
K-anonymisation is an approach to protecting privacy contained within a dataset. A good k-anonymisation algorithm should anonymise a dataset in such a way ...
A data set is k-anonymous (k ≥ 1) if each record in the data set is indistinguishable from at least (k − 1) other records within the same data set. The larger ...
Apr 14, 2021 · K-anonymity has evolved to become a highly effective form of privacy protection. Learn the best practices for implementing k-anonymity.
The paper aims to develop an approach to achieve k-anonymity that generalizes attributes based on their utility with the goal of minimum distortion of the ...
It works by building a lattice based on generalization and traverse it by bottom up breadth first order and after traversing whole lattice returns anonymized ...
A k-anonymization algorithm is usually evaluated using information loss or data utility metrics. In this paper, we first propose a new quality metric, called ...
The most common approach in this domain is to apply generalizations on the private data in order to maintain a privacy standard such as k-anonymity. While ...