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K-anonymization essentially is some update operations over the original dataset. So, to guarantee the integrity of the dataset, it's necessary to preserve the functional dependencies (FDs) in k-anonymization. We present several approaches to maintain FDs in k-anonymization. One is detecting FDs violation constantly while k-anonymizing, which can be merged to numerous previous k-anonymized algorithms...
Although k-anonymity can guarantee the security of privacy, it may violate data dependencies, such as FDs (functional dependency) in k-anonymization. We define a new data dependency named k-multiset dependency (K-MSD), and show that a K-MSD dataset satisfies k-anonymity constraint too. So, it is possible to implement k-anonymization through constructing K-MSDs over original dataset. For the FDs over...
We address how to capture the attribute preferences and maintain higher precision of the anonymized table. At first, we define a new data dependency named k-multiset dependency (K-MSD), and show that if a dataset satisfies K-MSD then it also satisfies k-anonymity constraint. Then, we present minimal distance generalization (MDG) to construct K-MSD between attributes. In addition, we propose a local...
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