Decryption Thorough Polynomial Ambiguity: Noise-Enhanced High-Memory Convolutional Codes for Post-Quantum Cryptography

We present a novel approach to post-quantum cryptography that employs directed-graph decryption of noise-enhanced high-memory convolutional codes. The proposed construction generates random-like generator matrices that effectively conceal algebraic structure and resist known structural attacks...

EUVD-2024-25043

Malicious code in bioql PyPI...

Towards Trustworthy Federated Learning with Untrusted Participants

Resilience against malicious participants and data privacy are essential for trustworthy federated learning, yet achieving both with good utility typically requires the strong assumption of a trusted central server. This paper shows that a significantly weaker assumption suffices: each pair of...

Shadow Defense against Gradient Inversion Attack in Federated Learning

Federated learning FL has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is...

Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems Using Explainable AI

Federated Learning FL has emerged as a powerful paradigm for collaborative model training while keeping client data decentralized and private. However, it is vulnerable to Data Reconstruction Attacks DRA such as "LoKI" and "Robbing the Fed", where malicious models sent from the server to the clie...

XBreaking: Explainable Artificial Intelligence for Jailbreaking LLMs

Large Language Models are fundamental actors in the modern IT landscape dominated by AI solutions. However, security threats associated with them might prevent their reliable adoption in critical application scenarios such as government organizations and medical institutions. For this reason,...

Mutual Information Minimization for Side-Channel Attack Resistance Via Optimal Noise Injection

Side-channel attacks SCAs pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electromagnetic emissions. Among existing countermeasures, artificial noise injection is recognized as one of the most effective...

SUSE CVE-2024-27850

This issue was addressed with improvements to the noise injection algorithm. This issue is fixed in Safari 17.5, iOS 17.5 and iPadOS 17.5, macOS Sonoma 14.5, visionOS 1.2. A maliciously crafted webpage may be able to fingerprint the user...

CVE-2024-27850

This issue was addressed with improvements to the noise injection algorithm. This issue is fixed in visionOS 1.2, macOS Sonoma 14.5, Safari 17.5, iOS 17.5 and iPadOS 17.5. A maliciously crafted webpage may be able to fingerprint the user...

UBUNTU-CVE-2024-27850

This issue was addressed with improvements to the noise injection algorithm. This issue is fixed in Safari 17.5, iOS 17.5 and iPadOS 17.5, macOS Sonoma 14.5, visionOS 1.2. A maliciously crafted webpage may be able to fingerprint the user...

CVE-2024-27850

CVE-2024-27850 is specified as resolved by Apple with fixes in visionOS 1.2, macOS Sonoma 14.5, Safari 17.5, iOS 17.5 and iPadOS 17.5. The issue arises from a flaw in the noise injection algorithm that potentially allows a malicious webpage to fingerprint a user. The connected documents corrobora...

CVE-2024-27850

This issue was addressed with improvements to the noise injection algorithm. This issue is fixed in Safari 17.5, iOS 17.5 and iPadOS 17.5, macOS Sonoma 14.5, visionOS 1.2. A maliciously crafted webpage may be able to fingerprint the user...

CVE-2024-27850

This issue was addressed with improvements to the noise injection algorithm. This issue is fixed in Safari 17.5, iOS 17.5 and iPadOS 17.5, macOS Sonoma 14.5, visionOS 1.2. A maliciously crafted webpage may be able to fingerprint the user...

CVE-2024-27850

Removed by vendor...