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.