Secure Two Party Privacy Preserving Classification Using Encryption
Keywords:
Privacy preserving, data mining, horizontal partitioned, paillier cryptosystem, RSA cryptosystem, secure computations, homomorphic property, commutative property.Abstract
Distributed computing demands training methods
that handle distributed input data. While training, as the parties that collaborate are concerned about the privacy of their data, the concept of privacy preservation should be extended in data mining classifiers. In this paper, data holders make practical use of their data to construct a precise classifier model by not revealing either their training data or the intermediate results.
We propose a privacy preserving two-party Naive Bayes
classifier for horizontally partitioned distributed data. This protocol is built such that both the parties through their random shares compute probabilities, mean and variance. To classify a new instance with numeric attributes, parties need to jointly cooperate with each other. The correctness and the security analysis of our algorithm are provided
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