Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
The ubiquitous need for analyzing privacy-sensitive information—including health records, personal communications, product ratings and social network data—is driving significant interest in privacy-preserving data analysis across several research communities. This paper explores the release of Supp...
Main Authors: | Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, Nina Taft |
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Format: | Article |
Language: | English |
Published: |
Labor Dynamics Institute
2012-07-01
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Series: | The Journal of Privacy and Confidentiality |
Subjects: | |
Online Access: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/612 |
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