An attention based approach for automated account linkage in federated identity management

Abstract

Linking digital accounts belonging to the same user has progressed from a research topic to a foundation for security, user satisfaction, and developing next-generation services. Still, few studies address account linkage in domains other than social networks. This deficiency is particularly apparent in federated domains such as academia, where network-based information and contextual data are typically unavailable. To address this issue, we propose SmartSSO, a framework that aims to automate the account linkage process by analyzing user routines and behavior during login processes. SmartSSO adapts two self-attention-based models to generate new representations from user patterns in a lower-dimensional latent space where the learned structure is employed to identify related accounts held by a user. We show that the trained models on a large corpus of production data, including more than one million samples gathered over six months from 50,000 users, achieve over 98% accuracy in hit-precision.