دانلود رایگان مقاله انگلیسی تجمیع ویژگی های حفظ حریم خصوصی در فدراسیون های eID - الزویر 2019

Abstract

Personalized electronic services, e.g. from the e-government domain, need to reliably identify and authenticate users. During user-authentication processes, the electronic identity of the respective user is determined and required additional attributes, e.g. name and date of birth, linked to this identity are collected. This attribute-collection process can become complex, especially if required attributes are distributed over various attribute providers that are organized in a federated identity-management system. In many cases, these identity management systems rely on different ontologies and make use of different languages. Hence, identity federations, such as the one currently established across the European Union, require effective solutions to collect user attributes from different heterogeneous sources and aggregate them to a holistic user facet. At the same time, these solutions need to comply with minimum disclosure rules to preserve users’ privacy. In this article, we propose and introduce a solution for privacy-preserving attribute aggregation. Our solution combines attributes from different domains using ontology alignment and makes use of locality sensitive hashing functions to preserve users’ privacy. Evaluation results obtained from conducted experiments demonstrate our solution’s advantages for both, service providers and users. While service providers can be provided with a larger set of attributes, users remain in full control of their data and can decide on which of their attributes shall be revealed.

 Conclusions

In this article, we have proposed a solution for the attribute aggregation problem in identity federations. The propose solution i) fits current deployed IdS scenarios, e.g. STORK, eIDAS; ii) is able to handle partially 790 federated identity systems (i.e. scenarios where some APs require local authentication), iii) supports entities (SPs and APs) relying on different ontologies and languages, iv) preserves users’ privacy while still providing results with high confidence levels. The ability to handle several languages represents a step forward to applying this solution in crosscountry scenarios. Although we have performed our experiments using English and German only, the 795 employed API supports more than 90 languages. Albeit the accuracy of our implementation depends on the performance of the API, we believe that possible inaccuracies from the translation process can be overcome by adopting lower threshold values in the first step of the ontology alignment process. By lowering the threshold boundaries in initial ontology alignment, the solution eliminates false negatives that may occur, even due to poor translations, leaving the confidence level improvement algorithm the task of eliminating 800 false positives.