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Item type:Publication, Access status: without files , Advancing blockchain-based federated learning through verifiable off-chain computationsFederated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of param-eters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.Publicationtype: Conference Paper78 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Blockchain-based data provenance for the internet of things(2019-10-22); ; ; ; As more and more applications and services depend on data collected and provided by Internet of Things (IoT) devices, it is of importance that such data can be trusted. Data provenance solutions together with blockchain technology are one way to make data more trustworthy. However, current solutions do not address the heterogeneous nature of IoT applications and their data. In this work, we identify functional and non-functional requirements for a generic IoT data provenance framework, and conceptualise the framework as a layered architecture. Using a proof-of-concept implementation based on Ethereum smart contracts, data provenance can be realised for a wide range of IoT use cases. Benefits of a generic framework include simplified adoption and a more rapid implementation of data provenance for the IoT.Publicationtype: Conference Paper29