Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computations

Heiss, Jonathan; Grünewald, Elias; Haimerl, Nikolas; Schulte, Stefan; Tai, Stefan

doi:10.48550/arxiv.2206.11641

Cited by 1 publication

(2 citation statements)

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“…Rachakonda et al [123] combined a proposed FL framework for the protection of privacy in IoHT devices with an SMPC algorithm to avoid reverse engineering data leakage attacks via model updates. In [124], the authors proposed a model for blockchain-based FL that leverages verifiable off-chain computations (VOCs) using ZKPs to enhance privacy in decentralized applications.…”

Section: Discussionmentioning

confidence: 99%

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Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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Section: Discussionmentioning

confidence: 99%

“…Heiss et al [124] proposed a model for blockchain-based FL that leverages verifiable off-chain computations (VOCs) using ZKPs. The architecture enables the computational correctness of local learning processes verifiable on the blockchain and provides globally verifiable management of global learning parameters.…”

Section: Cryptographic Methodsmentioning

confidence: 99%