Related papers: One For All: Formally Verifying Protocols which us…

WebAssembly is a binary format for code that is gaining popularity thanks to its focus on portability and performance. Currently, the most common use case for WebAssembly is execution in a browser. It is also being increasingly adopted as a…

Decentralized Finance (DeFi) has emerged as a contemporary competitive as well as complementary to traditional centralized finance systems. As of 23rd January 2024, per Defillama approximately USD 55 billion is the total value locked on the…

Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain…

Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the…

Publish/subscribe systems play a key role in enabling communication between numerous devices in distributed and large-scale architectures. While widely adopted, securing such systems often trades portability for additional integrity and…

The current verification flow of complex systems uses different engines synergistically: virtual prototyping, formal verification, simulation, emulation and FPGA prototyping. However, none is able to verify a complete architecture.…

We present the first performance comparison of EdDSA and BLS signatures in committee-based consensus protocols through large-scale geo-distributed benchmarks. Contrary to popular beliefs, we find that small deployments (less than 40…

Federated learning (FL) typically relies on synchronous training, which is slow due to stragglers. While asynchronous training handles stragglers efficiently, it does not ensure privacy due to the incompatibility with the secure aggregation…

Federated learning is a collaborative method that aims to preserve data privacy while creating AI models. Current approaches to federated learning tend to rely heavily on secure aggregation protocols to preserve data privacy. However, to…

In this paper we present the initial design of Minerva consensus protocol for Truechain and other technical details. Currently, it is widely believed in the blockchain community that a public chain cannot simultaneously achieve high…

Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model…

Large-scale decentralized learning frameworks such as federated learning (FL), require both communication efficiency and strong data security, motivating the study of secure aggregation (SA). While information-theoretic SA is well…

Signature verification is an authentication technique that considers handwritten signature as a biometric. From a biometric perspective this project made use of automatic means through an integration of intelligent algorithms to perform…

We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited…

This paper proposes a blockchain-based Federated Learning (FL) framework with Intel Software Guard Extension (SGX)-based Trusted Execution Environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL,…

Today, all types of digital signature schemes emphasis on secure and best verification methods. Different digital signature schemes are used in order for the websites, security organizations, banks and so on to verify user's validity.…

In Ethereum, the practice of verifying the validity of the passed addresses is a common practice, which is a crucial step to ensure the secure execution of smart contracts. Vulnerabilities in the process of address verification can lead to…

Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by expensive communication and privacy concerns. Both of these…

We present and characterize advanced attacks on an ensemble-based quantum token protocol that allows for implementing non-clonable quantum coins. Multiple differently initialized tokens of identically prepared qubit ensembles are combined…

Decentralized learning (DL) faces increased vulnerability to privacy breaches due to sophisticated attacks on machine learning (ML) models. Secure aggregation is a computationally efficient cryptographic technique that enables multiple…