arXiv AI
Jun 10
Post-Quantum Secure Federated DeFi for Inclusive Banking
arXiv:2606. 10658v1 Announce Type: cross Abstract: Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing.
Post-Quantum Secure Federated DeFi for Inclusive Banking
Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems.
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arXiv AI
Jun 9
Blockchain Infrastructure for Intelligent Cyber--Physical--Social Systems:Post-Quantum Security, Interoperability, and Trustworthy Data Economies in the Era of Embodied AI
arXiv:2606. 06895v1 Announce Type: cross Abstract: The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems.
arXiv Machine Learning
Jul 27
A Drift Stable Quantum Federated Learning for Intelligent Services
arXiv:2607. 21647v1 Announce Type: new Abstract: Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services.
Hugging Face Trending Papers
Jul 7
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, making them susceptible to both server-side inference and client-side poisoning attacks.
arXiv AI
Jul 9
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
arXiv:2607. 06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy.
Hugging Face Trending Papers
Jun 21
Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks
Fully homomorphic encryption (FHE) allows computations to be performed directly on encrypted data without decryption, offering strong privacy guarantees for sensitive data analysis. This capability is important for privacy-sensitive applications like secure cloud computing, finance, and healthcare.
arXiv AI
Jun 15
Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach
arXiv:2606. 14515v1 Announce Type: cross Abstract: Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns.
arXiv AI
Jun 26
TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems
arXiv:2606. 26664v1 Announce Type: cross Abstract: Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evaluations to at most ~20k nodes and making them incompatible with dynamic, large-scale financial graphs.
arXiv Machine Learning
Jul 7
Routing Anonymity and Identifiability of Noisy Quantum Hardware
arXiv:2607. 05281v1 Announce Type: cross Abstract: Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware.
arXiv Machine Learning
Jun 8
Privacy Implies Stability: Information-Theoretic Generalization Bounds for Quantum Learning
arXiv:2602. 01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms.
Hugging Face Trending Papers
Jun 23
ComputeFHE: A Privacy-Preserving General-Purpose Computation Library
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and development complexity.
arXiv AI
Jun 2
Silent Failures in Federated Personalization of Foundation Models
arXiv:2606. 00947v1 Announce Type: cross Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing regulatory requirements for post-market monitoring.