Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases
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@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}
}
TY - JOUR
T1 - Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases
AU - Alka Gangrade and Ravindra Patel
JO - Journal of Information and Computing Science
VL - 2
SP - 119
EP - 129
PY - 2024
DA - 2024/01
SN - 8
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22621.html
KW - Privacy preserving, Naïve Bayes classification, probability, secure multiplication protocol.
AB - 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.
Alka Gangrade and Ravindra Patel. (2024). Privacy Preserving Three-Layer Naïve Bayes Classifier for Vertically Partitioned Databases.
Journal of Information and Computing Science. 8 (2).
119-129.
doi:
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