Differentiated security levels for personal identifiable information in identity management system

With the rapid development of Internet services, identity management (IdM) has got widely attraction as the credit agency between users and service providers. It facilitates users to use the Internet service, promotes service providers to enrich services, and makes Internet more security. Personally identifiable information (PII) is the most important information asset with which identity provider (IdP) can provide various services. Since PII is sensitive to users, it has become a serious problem that PII is leaked, illegal selected, illegal accessed. In order to improve security of PII, this study develops a novel framework using data mining to forecast information asset value and find appropriate security level for protecting user PII. The framework has two stages. In the first stage, user information asset is forecasted by data mining tool (decision tree) from PII database. Then security level for user PII is determined by the information asset value assuming that the higher information asset is, the more security requirement of PII is. In the second stage, with time being, number of illegal access and attack can be accumulated. It can be used to reconstruct the decision tree and update the knowledge base combined with the result of the first stage. Thus security level of PII can be timely adjusted and the protection of PII can be guaranteed even when security threat changes. Furthermore, an empirical case was studied in a user dataset to demonstrate the protection decision derived from the framework for various PII. Simulation results show that the framework with data mining can protect PII effectively. Our work can benefit the development of e-business service.