Classification with Meta-learning in Privacy Preserving Data Mining

摘要

In privacy preserving classification, when data is stored in a centralized database and distorted using a randomization-based technique, we have information loss and reduced accuracy of classification. Moreover, there are several possible algorithms, different reconstruction types (in case of decision tree) to use and we cannot point out the best combination of them. Meta-learning is the solution to combine information from all algorithms. Furthermore, it gives higher accuracy of classification. This paper presents the new meta-learning approach to privacy preserving classification for centralized data. Effectiveness of this solution has been tested on real data sets and presented in this paper.