Visible to the public Using Probabilistic Attribute Aggregation for Increasing Trust in Attribute Assurance

TitleUsing Probabilistic Attribute Aggregation for Increasing Trust in Attribute Assurance
Publication TypeConference Paper
Year of Publication2019
AuthorsGrüner, Andreas, Mühle, Alexander, Meinel, Christoph
Conference Name2019 IEEE Symposium Series on Computational Intelligence (SSCI)
KeywordsAggregates, aggregation model, attribute aggregation method, attribute assurance, authentication, composability, Credit cards, decentralized peer to peer scheme, Digital identity, identity assurance, Identity management, identity management attribute aggregation, Identity provider, Internet, Logic gates, online services, Peer-to-peer computing, probabilistic attribute aggregation, Probabilistic logic, probability distributions, pubcrawl, Resiliency, security of data, self-sovereign identity solutions, service providers, Service Provisioning, statistical distributions, Trust, trust demand, trust model, trust requirement, Trusted Computing, trusted third party, web of trust
AbstractIdentity management is an essential cornerstone of securing online services. Service provisioning relies on correct and valid attributes of a digital identity. Therefore, the identity provider is a trusted third party with a specific trust requirement towards a verified attribute supply. This trust demand implies a significant dependency on users and service providers. We propose a novel attribute aggregation method to reduce the reliance on one identity provider. Trust in an attribute is modelled as a combined assurance of several identity providers based on probability distributions. We formally describe the proposed aggregation model. The resulting trust model is implemented in a gateway that is used for authentication with self-sovereign identity solutions. Thereby, we devise a service provider specific web of trust that constitutes an intermediate approach bridging a global hierarchical model and a locally decentralized peer to peer scheme.
DOI10.1109/SSCI44817.2019.9003094
Citation Keygruner_using_2019