  Identity tracing is a technology that uses the selection and collection of
identity attributes of the object to be tested to discover its true identity,
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, where deep learning-based model always lacks interpretability.
Multivariate attribute collaborative identification is a possible key way to
overcome the mentioned recognition errors and low data quality problems. 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 cope with the lack
of test data, we construct a dataset of 1000 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.
