Employing Ontology-Alignment and Locality-Sensitive Hashing to Improve Attribute Interoperability in Federated eID Systems

Abstract

Abstract. Achieving interoperability, i.e. creating identity federations between different Electronic identities (eID) systems, has gained relevance throughout the past years. A serious problem of identity federations is the missing harmonization between various attribute providers (APs). In closed eID systems, ontologies allow a higher degree of automation in the process of aligning and aggregating attributes from different APs. This approach does not work for identity federations, as each eID system uses its own ontology to represent its attributes. Furthermore, providing attributes to intermediate entities required to align and aggregate attributes potentially violates privacy rules. To tackle these problems, we propose the use of combined ontology-alignment (OA) approaches and locality-sensitive hashing (LSH) functions. We assess existing implementations of these concepts defining and using criteria that are special for identity federations. Obtained results confirm that proper implementations of these concepts exist and that they can be used to achieve interoperability between eID systems on attribute level. A prototype is implemented showing that combining the two assessment winners (AlignAPI for ontology-alignment and Nilsimsa for LSH functions) achieves interoperability between eID systems. In addition, the improvement obtained in the alignment process by combining the two assessment winners does not impact negatively the privacy of the user's data, since no clear-text data is exchanged in the alignment process.