A self-adaptation data publishing algorithm framework

We present a new self-adaptation data publishing framework based on the concept of personalized anonymity for a certain application field (e.g. digital library). Our technique applies a self-adaptive mechanism to meet the needs of the different data applications with the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the metadata. We propose an algorithm framework for computing a generalized table with small information loss, which guarantees the appropriate breach probability for each tuple under the guidance of domain knowledge. The strategy of the sensitive attribute generalization and quasi-attribute generalization is well applied in the algorithm.