Social Network Relational Vectors for Anonymous Identity Matching

Anonymous fraudulent behavior can generate substantial financial burden and inconvenience. Moreover, the recent threat of terrorist infiltration to both business and government has yielded heightened interest in anonymous identity matching (AIM). Most applications of AIM require sophisticated methods robust to issues such as deliberate variation in identity attributes, missing data, and multi-source data corpora. We consider relational social network behavior, eliminating the reliance on personal identifiable data for identity matching. In particular, we consider problems that can be characterized by personal communication networks. We evaluate a proposed implementation of a social network vector-space relational model for AIM on CiteSeer, a research publication citation database.