کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
724976 | 1461233 | 2013 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Differentially private feature selection under MapReduce framework
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی برق و الکترونیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Privacy preserving data mining algorithms are crucial for the personal data analysis, such as medical and financial records. This paper focuses on feature selection and proposes a new privacy preserving distributed algorithm, which can effectively select features based on differential privacy and Gini index under the MapReduce framework. At the same time, the theoretic analysis for privacy guarantee is also presented. Some experiments are conducted on bench-mark datasets, the simulation results indicate that during the selection of important features, the proposed algorithm can preserve privacy information to a certain extent with less time cost than on centralized counterpart.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: The Journal of China Universities of Posts and Telecommunications - Volume 20, Issue 5, October 2013, Pages 85-90, 103
Journal: The Journal of China Universities of Posts and Telecommunications - Volume 20, Issue 5, October 2013, Pages 85-90, 103
نویسندگان
Kai CHEN, Wen-qiang WAN, Yun LI,