Privacy Preserving Naïve Bayesian Classifier For Horizontal Partitioned Data
Sumana M. 1 and
Hareesha K. S. 2
1. M S Ramaiah Institute of Technology/Information Science and Engineering, Bangalore, India
2. Manipal Institute of Technology/Department of Computer Applications, Manipal,India
2. Manipal Institute of Technology/Department of Computer Applications, Manipal,India
Abstract—Data is distributed in various sites that need to be mined in a secure manner without revealing anything except the results of mining. This paper converses about privacy-preserving horizontal distributed classification techniques where multiple sites collaborate and broadcast the mining results. However in the process, no information about either the data maintained in the sites or data obtained during computation is divulged. We have presented two protocols to construct a Privacy Preserving Naïve Bayesian classifier using the Pailler’s homomorphic encryption techniques. We propose that our approach is more secure and efficient than any of the previous privacy preserving Naïve Bayesian methods.
Index Terms—secure sum, homomorphic encryption, paillier encryption, privacy preserving data mining, naïve Bayesian, horizontal partitioned
Cite: Sumana M. and Hareesha K. S., "Privacy Preserving Naïve Bayesian Classifier For Horizontal Partitioned Data," International Journal of Electronics and Electrical Engineering, Vol. 2, No. 1, pp. 21-25, March 2014. doi: 10.12720/ijeee.2.1.21-25
Index Terms—secure sum, homomorphic encryption, paillier encryption, privacy preserving data mining, naïve Bayesian, horizontal partitioned
Cite: Sumana M. and Hareesha K. S., "Privacy Preserving Naïve Bayesian Classifier For Horizontal Partitioned Data," International Journal of Electronics and Electrical Engineering, Vol. 2, No. 1, pp. 21-25, March 2014. doi: 10.12720/ijeee.2.1.21-25
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