Privacy-Preserving eID Derivation for Self-Sovereign Identity Systems

As centralized identity management solutions amass identity data, they increasingly become attractive targets for cyber attacks, which entail consequences for users that range from service disruptions to exposure of sensitive user data. Self-sovereign identity (SSI) strives to return the control over identity data to the users by building on decentralized architectures. However, the adoption of SSI systems is currently hampered by a lack of qualified identity data that satisfies the services’ requirements. Additionally, there is a gap w.r.t the user’s privacy: Intermediate components (e.g., importers or SSI network nodes) learn the users’ sensitive attributes during the derivation of eID data.

In this work, we present a decentralized eID derivation concept that preserves the users’ privacy while maintaining the data’s trustworthiness without revealing the plain data to any component outside the users’ control. Our proposed system also enables users to selectively disclose only relevant parts of the imported identity assertion according to the service’s requirements. We also implement and evaluate a proof-of-concept to demonstrate the feasibility and performance of our concept.