In this paper, we propose a modification to the hierarchical signature scheme that enhances the scalability and integrity of the resulting anonymous signature hierarchy, while introducing a new feature: the ability to hide additional signatures. We leverage blockchain infrastructure and smart contracts to improve the integrity of the hierarchy, address scalability challenges, and facilitate efficient node verification. We conceptualize the blockchain as a management layer that enables the selective revelation of non-signers within a ring of nodes by disclosing their ephemeral values. This process refines the anonymity set and allows for the recovery of hidden signatures. Furthermore, we present a performance analysis based on a proof-of-concept implementation of hierarchical signatures on the Ethereum blockchain, evaluating operational costs to demonstrate the scheme's efficiency.

In this paper we propose a novel method for achieving verifiable and robust distributed deep learning. Agents verify model correctness and detect attacks, while a blockchain enforces consensus. The approach comprises two core elements: a suite of local validation mechanisms that produce unique "integrity signatures" from trained models, and a smart contract-driven protocol for managing node participation and collective validation. We designed and assessed several validator types, including those analyzing confusion matrices, model parameters, and responses to targeted input perturbations. A prototype was implemented with TensorFlow and Ethereum to evaluate the system’s resilience. Through extensive experiments on Fashion-MNIST with MLP and CNN models under data poisoning attacks, we demonstrate the method’s efficacy. A key finding is that a validator using test set confusion matrices can reliably identify malicious nodes, achieving perfect recall and a low false positive rate against single-class poisoning attacks. Our method successfully identifies and rejects malicious contributions, ensuring the integrity of the collaborative learning process.

Provable Data Possession (PDP) mechanisms allows for efficient verification of data integrity in remote storage platforms. In this paper, we show an efficient way to use a secure PDP scheme for cloud storage with blockchain technology. In our system, verification nodes in the blockchain network challenge data centers through distributed PDP transactions. We show that our method is effective, which is confirmed by experimental results.

In this study, we explore the potential of integrating blockchain technology and cryptographic primitives such as Schnorr signatures and Pedersen commitments, via a Stamp and Extend scheme, in order to develop a trusted and reliable timestamping system. Our proposed architecture aims to facilitate the safe and reliable platooning of dangerous goods vehicles in C-ITS enabled tunnels. We have implemented a proof-of-concept on the Ethereum platform, demonstrating the feasibility and survivability of our proposed architecture. A series of performance experiments further underscore the potential of our system, reinforcing its value in fostering secure and trusted coordination of dangerous goods transport in connected vehicle tunnels.