An effective deferentially private data releasing algorithm for decision tree

Zhu, Tianqing, Xiong, Ping, Xiang, Yang and Zhou, Wanlei 2013, An effective deferentially private data releasing algorithm for decision tree, in TrustCom 2013 : Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Computer Society, Piscataway, N.J., pp. 388-395.

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Title An effective deferentially private data releasing algorithm for decision tree
Author(s) Zhu, Tianqing
Xiong, Ping
Xiang, Yang
Zhou, Wanlei
Conference name Trust, Security and Privacy in Computing and Communications. IEEE Conference (12th : 2013 : Melbourne, Victoria)
Conference location Melbourne, Victoria
Conference dates 16-18 Jul. 2013
Title of proceedings TrustCom 2013 : Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Start page 388
End page 395
Total pages 8
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Keyword(s) Decision Tree
Differential Privacy
Privacy Preserving
Summary Differential privacy is a strong definition for protecting individual privacy in data releasing and mining. However, it is a rigid definition introducing a large amount of noise to the original dataset, which significantly decreases the quality of data mining results. Recently, how to design a suitable data releasing algorithm for data mining purpose is a hot research area. In this paper, we propose a differential private data releasing algorithm for decision tree construction. The proposed algorithm provides a non-interactive data releasing method through which miner can obtain the complete dataset for data mining purpose. With a given privacy budget, the proposed algorithm generalizes the original dataset, and then specializes it in a differential privacy constrain to construct decision trees. As the designed novel scheme selection operation can fully utilize the allocated privacy budget, the data set released by the proposed algorithm can yield better decision tree models than other method. Experimental results demonstrate that the proposed algorithm outperforms existing methods for private decision tree construction.
ISBN 9780769550220
Language eng
Field of Research 080503 Networking and Communications
080501 Distributed and Grid Systems
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061639

Document type: Conference Paper
Collection: School of Information Technology
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