A framework of identity resolution: evaluating identity attributes and matching algorithms

Abstract

Duplicate and false identity records are quite common in identity management systems due to unintentional errors or intentional deceptions. Identity resolution is to uncover identity records that are co-referent to the same real-world individual. In this paper we introduce a framework of identity resolution that covers different identity attributes and matching algorithms. Guided by social identity theories, we define three types of identity cues, namely personal identity attributes, social behavior attributes, and social relationship attributes. We also compare three matching algorithms: pair-wise comparison, transitive closure, and collective clustering. Our experiments using synthetic and real-world data demonstrate the importance of social behavior and relationship attributes for identity resolution. In particular, a collective identity resolution technique, which captures all three types of identity attributes and makes matching decisions on identities collectively, is shown to achieve the best performance among all approaches.