Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Semantic Web in the Context Broker Architecture
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Privacy-Aware Proximity Based Services
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Modeling and evaluating information leakage caused by inferences in supply chains
Computers in Industry
Journal of Intelligent Manufacturing
A model based transformation paradigm for cross-language collaborations
Advanced Engineering Informatics
Hi-index | 0.00 |
In global recession, outsourcing becomes a question of survival for most executives who need to restore profitability and growth. One of the critical challenges faced by such decisions is the potential risk of leaking confidential information through shared suppliers and partners. In this paper, a new approach is proposed to decompose a product into several sub-components for mitigating the risk of Intellectual Property (IP) leakage caused by inferences in supply chains. A design structure matrix is employed to study the potential risk of IP leakage considering different types of interactions between product components. Based on such a matrix, a clustering algorithm is developed to decompose and allocate the product components regarding IP protection issue. This methodology can be considered as a decision support tool to help the manufacturer select a set of optimal suppliers while minimizing the information leakage risk and the manufacturing cost.