Privacy preserving naive Bayes classification

Privacy preserving data mining is to discover accurate patterns without precise access to the original data. This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction. An ERRPH (Extended Randomized Response with Partial Hiding) method and a TRR (Transforming Randomized Response) method are respectively presented for enumerated data and numerical data. Then, a privacy preserving Naive Bayes classification algorithm is implemented based on those methods. Theoretical analyses show that it can provide better privacy, accuracy, efficiency, and applicability. The effectiveness is also verified by experiments.EI081267-12763