Related papers: Securing HPC using Federated Authentication
Approximately 61% of cyber attacks involve adversaries in possession of valid credentials. Attackers acquire credentials through various means, including phishing, dark web data drops, password reuse, etc. Multi-factor authentication (MFA)…
We present a quantum multi-factor authentication mechanism based on the hidden-matching quantum communication complexity problem. It offers step-up graded authentication for users via a quantum token. In this paper, we outline the protocol,…
Multi-Access or Mobile Edge Computing (MEC) is being deployed by 4G/5G operators to provide computational services at lower latencies. Federating MECs across operators expands capability, capacity, and coverage but gives rise to two issues…
Metaverse in general holds a potential future for cyberspace. At the beginning of Web 2.0, it was witnessed that people were signing in with various pseudonyms or 'nyms', risking their online identities by increasing presence of fake…
In password-based authentication systems, the username fields are essentially unprotected, while the password fields are susceptible to attacks. In this article, we shift our research focus from traditional authentication paradigm to the…
Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication…
The digital age requires strong security measures to protect online activities. Two-Factor Authentication (2FA) has emerged as a critical solution. However, its implementation presents significant challenges, particularly in terms of…
In the age of data-driven decision making, preserving privacy while providing personalized experiences has become paramount. Personalized Federated Learning (PFL) offers a promising framework by decentralizing the learning process, thus…
The majority of current web authentication is built on username/password. Unfortunately, password replacement offers more security, but it is difficult to use and expensive to deploy. In this paper, we propose a new mutual authentication…
This work focuses on the problem of detection and prevention of stolen and misused secrets (such as private keys) for authentication toward centralized services. We propose a solution for such a problem based on the blockchain-based…
Business analytics processes are often composed from orchestrated, collaborating services, which are consumed by users from multiple cloud systems (in different security realms), which need to be engaged dynamically at runtime. If…
The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integrity and participant privacy. This study proposes an…
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it…
Decentralized systems can be more resistant to operator mischief than centralized ones, but they are substantially harder to develop, deploy, and maintain. This cost is dramatically reduced if the decentralized part of the system can be…
The prevalence of biometric authentication has been on the rise due to its ease of use and elimination of weak passwords. To date, most biometric authentication systems have been designed for on-device authentication of the device owner…
Federated learning is a distributed learning setting where the main aim is to train machine learning models without having to share raw data but only what is required for learning. To guarantee training data privacy and high-utility models,…
Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold…
Usable and secure authentication on the web and beyond is mission-critical. While password-based authentication is still widespread, users have trouble dealing with potentially hundreds of online accounts and their passwords. Alternatives…
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or the sheer volume of data generated. Federated learning (FL)…
In federated learning, multiple parties collaborate in order to train a global model over their respective datasets. Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some…