Reconstruction-Based Association Rule Hiding

As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data mining problems. Compared with traditional data modification methods, data reconstruction is a new promising, but not sufficiently investigated method, which is inspired by the inverse frequent set mining problem. In my PhD period, my research focuses on further investigating reconstruction-based techniques for association rule hiding. Particularly, we have proposed a FP-treebased method for inverse frequent set mining, which can be used in our proposed reconstruction-based framework. We hope the proposed solution will fetch up the new reconstruction-based research track and work well according to the evaluation metrics including hiding effects, data utility, and time performance.