Toward trustworthy identity tracing via multi-attribute synergistic identification

Abstract

Identity tracing is a method used to determine the true identity of a subject by selecting and analyzing its relevant attributes, and it is one of the most important foundational issues in the field of social security prevention. However, traditional identity recognition technologies based on single attributes have difficulty achieving ultimate recognition accuracy, whereas deep learning-based models tend to lack interpretability. Collaborative identification using multiple attributes offers a viable approach to address these limitations and resolve issues related to data quality. In this paper, we propose the “Trustworthy Identity Tracing” (TIT) task and a Multi-attribute Synergistic Identification based TIT framework. We first established a novel identity model based on identity entropy theoretically. The individual conditional identity entropy and core identification set are defined to reveal the intrinsic mechanism of multivariate attribute collaborative identification. Based on the proposed identity model, we propose a trustworthy identity tracing framework (TITF) with multi-attribute synergistic identification to determine the identity of unknown objects, which can optimize the core identification set and provide an interpretable identity tracing process. Actually, the essence of identity tracing is revealed to be the process of the identity entropy value converging to zero. To compensate for the lack of test data, we construct a dataset of 5000 objects to simulate real-world scenarios, where 20 identity attributes are labeled to trace unknown object identities. The experiment results conducted on the mentioned dataset show the proposed TITF algorithm can achieve satisfactory identification performance. Furthermore, the proposed TIT task explores the interpretable quantitative mathematical relationship between attributes and identity, broadening identity representation from a single-attribute domain to a multi-attribute collaborative domain. It indeed provides a novel perspective for realizing credible and interpretable identification techniques in real-world scenarios.