Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases

Data mining is the extraction of the hidden information from large databases. It is a powerful technology to explore important information in the data warehouse. Privacy preservation is a significant problem in the field of data mining. It is more challenging when data is distributed among different parties. In this paper, we address the problem of privacy preserving three-layer Naive Bayes classification over vertically partitioned data. Our approach is based on Secure Multiparty Computation (SMC). We use secure multiplication protocol to classify the new tuples. In our protocol, secure multiplication protocol allows to meet privacy constraints and achieve acceptable performance and our classification system is very efficient in term of computation and communication cost.

[1]  Chris Clifton,et al.  Privacy-Preserving Decision Trees over Vertically Partitioned Data , 2005, DBSec.

[2]  Divyakant Agrawal,et al.  Privacy preserving decision tree learning over multiple parties , 2007, Data Knowl. Eng..

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[4]  Chris Clifton,et al.  Privacy preserving data mining over vertically partitioned data , 2004 .

[5]  Chris Clifton,et al.  Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data , 2004, SDM.

[6]  Amit Sahai,et al.  Secure Multi-Party Computation , 2013 .

[7]  Alka Gangrade,et al.  Privacy Preserving Two-Layer Decision Tree Classifier for Multiparty Databases , 2012 .

[8]  Bingru Yang,et al.  Privacy Preserving Decision Tree Learning over Vertically Partitioned Data , 2008, 2008 International Conference on Computer Science and Software Engineering.

[9]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[10]  Marina Blanton,et al.  Secure Multiparty Computation , 2011, Encyclopedia of Cryptography and Security.

[11]  Ramakrishnan Srikant,et al.  Privacy-preserving data mining , 2000, SIGMOD '00.

[12]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[15]  M. Atallah,et al.  Secure Multi-Problem Computation Problems and Their Applications : A Review and Open Problems , 2001 .

[16]  Rebecca N. Wright,et al.  Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data , 2006, IEEE Transactions on Knowledge and Data Engineering.

[17]  Jaideep Vaidya,et al.  Privacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data , 2003 .

[18]  Wenliang Du,et al.  Secure multi-party computation problems and their applications: a review and open problems , 2001, NSPW '01.

[19]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[20]  Elisa Bertino,et al.  State-of-the-art in privacy preserving data mining , 2004, SGMD.

[21]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[22]  Alka Gangrade.  A Novel Protocol For Privacy Preserving Decision Tree Over Horizontally Partitioned Data , 2011 .

[23]  Wenliang Du,et al.  Building decision tree classifier on private data , 2002 .

[24]  Chris Clifton,et al.  Tools for privacy preserving distributed data mining , 2002, SKDD.