A STUDY ON BIG DATA BASED NON-FACE-TO-FACE IDENTITY PROOFING MODEL

摘要

Online service providers are increasingly considering the adoption of a variety of additional mechanisms to supplement the authentication security provided by conventional password verification. Recently, the authentication and authorization methods using the user attribute information have been used for various services. In particular, the need for various approaches to non-face-to-face identification technology for online user registering and authentication are increasing demands because of the growth of online financial services and the rapid development of financial technology. However, non-face-to-face approaches can be generally exposed to a greater number of threats than face-to-face approaches. Therefore, identification policies and technologies to verify users by using various factors and channels are being studied in order to complement the risks and to be more reliable non-face-to-face identification methods. One of these new approaches is to collect and verify a large number of personal information of user. Thus, we propose a big-data based non-face-to-face Identity Proofing model that verifies identity on online based on various and large amount of information of user. The proposed model performs identification of various attribute information required for the identity verification level. In addition, the proposed model can be quantified identity proofing reliability as collects and verifies only the user information required for assurance level of identity proofing.