Preserving Privacy in Supply Chain Management: A Challenge for Next Generation Data Mining

• Main Authors: Ahluwalia, Madhu, CHEN, Zhiyuan, Gangopadhyay, Arrya, GUO, Zhiling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Computer Sciences; Management Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/1870; https://ink.library.smu.edu.sg/context/sis_research/article/2869/viewcontent/GuoZ2007MAhluwalia.pdf
• Institution: Singapore Management University

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.