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Differentially private multi-task learning

Gupta, Sunil, Rana, Santu and Venkatesh, Svetha 2016, Differentially private multi-task learning, in PAISI 2016 : Intelligence and Security Informatics : Proceedings of the 11th Pacific-Asia Workshop, Springer International Publishing, Cham, Switzerland, pp. 101-113, doi: 10.1007/978-3-319-31863-9_8.

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Title Differentially private multi-task learning
Author(s) Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Venkatesh, Svetha
Conference name Intelligence and Security Informatics. Pacific-Asia Workshop (11th : 2016 : Auckland, New Zealand)
Conference location Auckland, New Zealand
Conference dates 19. Apr. 2016
Title of proceedings PAISI 2016 : Intelligence and Security Informatics : Proceedings of the 11th Pacific-Asia Workshop
Editor(s) Chau, Michael
Wang, G. Alan
Chen, Hsinchun
Publication date 2016
Series Lecture notes in computer science
Conference series Intelligence and Security Informatics Pacific-Asia Workshop
Start page 101
End page 113
Total pages 13
Publisher Springer International Publishing
Place of publication Cham, Switzerland
Summary Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.
ISBN 9783319318639
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-31863-9_8
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, Springer International Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094579

Document type: Conference Paper
Collection: School of Information Technology
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