Privacy Preserving Naïve Bayes Classifier for Vertically Partitioned Data
نویسندگان
چکیده
Privacy-Preserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Näıve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This paper brings privacy-preservation to Näıve Bayes classification on vertically partitioned data.
منابع مشابه
Performance Analysis of Privacy Preserving Naïve Bayes Classifiers for Distributed Databases
The problem of secure and fast distributed classification is an important one. The main focus of the paper is on privacy preserving distributed classification rule mining. This research paper addresses the performance analysis of privacy preserving Naïve Bayes classifiers for horizontal and vertical partitioned databases. The Naïve Bayes classifier is a simple but efficient baseline classifier....
متن کاملPrivacy Preserving Näıve Bayes Classifier for Vertically Partitioned Data
Privacy-Preserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Näıve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This...
متن کاملPrivacy Preserving Naïve Bayes Classifier for Horizontally Distribution Scenario Using Un-trusted Third Party
The aim of the classification task is to discover some kind of relationship between the input attributes and the output class, so that the discovered knowledge can be used to predict the class of a new unknown tuple. The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all ...
متن کاملSMC Protocol for Naïve Bayes Classification over Grid Partitioned Data using Multiple UTPs
The case where data is distributed horizontally as well as vertically, it refers as grid partitioned data. SMC protocol for Naïve Bayes classification over grid partitioned data is offered in this paper. Also present a solution of the Secure Multi-party Computation (SMC) problem in the form of a protocol that preserves privacy. In this system, a protocol with several Un-trusted Third Parties (U...
متن کاملPrivacy Preserving Naive Bayes Classifier for Horizontally Partitioned Data
The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline classifie...
متن کامل