Blockchain-Based Self-Sovereign Identity for Federated Learning in Vehicular Networks

E Zeydan, L Blanco, J Mangues… - … on Network and …, 2023 - ieeexplore.ieee.org
2023 19th International Conference on Network and Service …, 2023ieeexplore.ieee.org
Self-Sovereign Identity (SSI) has emerged lately as an identity and access management
framework that is based on Distributed Ledger Technology (DLT) and allows users to control
their own data. Federate Learning (FL), on the other hand, provides a framework to update
Machine Learning (ML) models without relying on explicit data exchange between the users.
This paper investigates identity management and authentication for vehicle users, which are
participating into FL. We propose a new approach to SSI, that is alternative to the …
Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.
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