Name
Abstract | ||
|---|---|---|
For meeting diverse requirements of data analysis, the machine learning classifier has been provided as a tool to evaluate data in many applications. Due to privacy concerns of preventing disclosing sensitive information, data owners often suppress their data for an untrusted trainer to train a classifier. Some existing work proposed privacy-preserving solutions for learning algorithms, which allow a trainer to build a classifier over the data from a single owner. However, they cannot be directly used in the multi-owner setting where each owner is not totally trusted for each other. In this paper, we propose a novel privacy-preserving Naive Bayes learning scheme with multiple data sources. The proposed scheme enables a trainer to train a Naive Bayes classifier over the dataset provided jointly by different data owners, without the help of a trusted curator. The training result can achieve ϵ-differential privacy while the training will not break the privacy of each owner. We implement the prototype of the scheme and conduct corresponding experiment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.02.056 | Information Sciences |
Keywords | Field | DocType |
Privacy-preserving,Naive Bayes classification,Differential privacy | Trainer,Multiple data,Naive Bayes classifier,Artificial intelligence,Classifier (linguistics),Information sensitivity,Mathematics,Machine learning,Learning classifier system | Journal |
Volume | ISSN | Citations |
444 | 0020-0255 | 50 |
PageRank | References | Authors |
1.03 | 34 | 5 |