Related papers: Securing HPC using Federated Authentication

Privacy-preserving data mining has become an important topic. People have built several multi-party-computation (MPC)-based frameworks to provide theoretically guaranteed privacy, the poor performance of real-world algorithms have always…

Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years. An important issue of face recognition is data privacy, which receives more and more public concerns. As a common…

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge…

Passwords have been long used as the primary authentication method for web services. Weak passwords used by the users have prompted the use of password management tools and two-factor authentication to ensure better account security. While…

The paper presents an authentication scheme for remote systems using smart card. The scheme prevents the scenario of many logged in users with the same login identity, and does not require password/verifier table to validate the users'…

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models only collecting the intermediate parameters instead of…

Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to…

Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing…

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.…

Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring…

Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an…

Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improved by ensuring that communication between the participants…

With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of…

Unequivocally, a single man in possession of a strong password is not enough to solve the issue of security. Studies indicate that passwords have been subjected to various attacks, regardless of the applied protection mechanisms due to the…

Decentralized cryptocurrency exchanges offer compelling security benefits over centralized exchanges: users control their funds and avoid the risk of an exchange hack or malicious operator. However, because user assets are fully accessible…

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new…

Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for…

Federated learning (FL) enables collaborative training across organizational silos without sharing raw data, making it attractive for privacy-sensitive applications. With the rapid adoption of large language models (LLMs), federated…

We introduce LOCATHE (Location-Enhanced Authenticated Key Exchange), a generic protocol that pools location, user attributes, access policy and desired services into a multi-factor authentication, allowing two peers to establish a secure,…

Cloud computing is a revolutionary concept that has brought a paradigm shift in the IT world. This has made it possible to manage and run businesses without even setting up an IT infrastructure. It offers multi-fold benefits to the users…