Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
10-2007
Abstract
In this paper we identify a major area of research as a topic for next generation data mining. The research effort in the last decade on privacy preserving data mining has resulted in the development of numerous algorithms. However, most of the existing research has not been applied in any particular application context. Hence it is unclear whether the current algorithms are directly applicable in any particular problem context. In this paper we identify a significant application context that not only requires protection of privacy but also sophisticated data analysis. The area in question is supply chain management, arguably one of the most important research areas in production and operations management that has enormous practical relevance. We examine the area of supply chain management and identify research challenges and opportunities for privacy preserving data mining in the next generation.
Discipline
Computer Sciences | Management Information Systems
Research Areas
Information Systems and Management
Publication
Proceedings of National Science Foundation Symposium on Next Generation Data Mining and Cyber-Enabled Discovery for Innovation NGDM 2007
First Page
1
Last Page
5
City or Country
Baltimore, Maryland
Citation
Ahluwalia, Madhu; CHEN, Zhiyuan; Gangopadhyay, Arrya; and GUO, Zhiling.
Preserving Privacy in Supply Chain Management: A Challenge for Next Generation Data Mining. (2007). Proceedings of National Science Foundation Symposium on Next Generation Data Mining and Cyber-Enabled Discovery for Innovation NGDM 2007. 1-5.
Available at: https://ink.library.smu.edu.sg/sis_research/1870
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.csee.umbc.edu/~hillol/NGDM07/abstracts/poster/MAhluwalia.pdf