@Article{JICS-8-119,
author = {Alka Gangrade and Ravindra Patel},
title = {Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {8},
number = {2},
pages = {119--129},
abstract = { 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  Naïve  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. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22621.html}
}