PrivateSphare

PrivateSphare PrivateSphare 是一个安全、匿名、零知识的文件存储与共享平台。用户可上传文件并获取一个人类可读的 24 词密码短语用于检索。无需账户、无追踪，且任何未加密数据都不会触及服务器的持久存储。 🛡️ 关键特性 后量子安全 ：文件采用量子安全混合 方案（Kyber1024 + AES-256-GCM）加密，可抵御未来量子计算机的威胁。 零知识加密 ：文件在浏览器上下文中（客户端意图）使用从 24 词 BIP-39 密码短语派生的密钥进行加密。 元数据隐私 ：文件名和 MIME 类型在存入数据库前已加密。 透明存储 ：文件以随机 UUID 密钥存储于...

PQCrypto-LWEKE

FrodoKEM: Encapsulación de Claves basada en Learning with Errors Esta biblioteca en C implementa FrodoKEM , un protocolo de encapsulación de claves KEM seguro bajo IND-CCA basado en el bien estudiado problema Learning with Errors LWE 1,3, que a su vez tiene estrechas conexiones con problemas...

QuantumLock

QuantumLock 坚不可摧，量子就绪，永恒。 QuantumLock 是一个开源、抗量子的比特币协议 ，旨在保护数字资产免受量子计算这一新兴威胁的影响。它融合了先进的密码学、隐私保护、可扩展性和弹性特性，确保比特币保持安全并面向未来。 目录 1. 概述 2. 功能特性 3. 安装 4. 使用 5. 贡献 6. 许可证 7. 署名 概述 量子计算对当前的密码学标准构成了切实的威胁。QuantumLock 通过引入抗量子签名、混合多重签名支持、模块化共识升级和隐私增强，确保后量子时代的比特币安全 。 它完全开源 ，旨在供全球开发者社区采纳、试验和贡献。 功能特性 核心抗量子能力...

The Race to Quantum-Proof the Internet Has Already Begun

The race to quantum-proof the internet is underway as experts warn of “harvest now, decrypt later” risks and slow migration to post-quantum security...

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...

Engel P-Adic Isogeny-Based Cryptography over Laurent Series: Foundations, Security, and an ESP32 Implementation

Securing the Internet of Things IoT against quantum attacks requires public-key cryptography that i remains compact and ii runs efficiently on microcontrollers, capabilities many post-quantum PQ schemes lack due to large keys and heavy arithmetic. We address both constraints simultaneously with, ...

Post-Quantum Security of Block Cipher Constructions

Block ciphers are versatile cryptographic ingredients that are used in a wide range of applications ranging from secure Internet communications to disk encryption. While post-quantum security of public-key cryptography has received significant attention, the case of symmetric-key cryptography and...

EUVD-2024-36566

Malicious code in bioql PyPI...

EUVD-2025-6671

Malicious code in bioql PyPI...

Threat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Framework

The rapid proliferation of IoT nodes equipped with microphones and capable of performing on-device audio classification exposes highly sensitive data while operating under tight resource constraints. To protect against this, we present a defence-in-depth architecture comprising a security protoco...

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving V2X Communication

Intelligent Transportation Systems ITS fundamentally rely on vehicle-generated data for applications such as congestion monitoring and route optimization, making the preservation of user privacy a critical challenge. Homomorphic Encryption HE offers a promising solution by enabling computation on...

Restricted Boltzmann Machine As a Probabilistic Enigma

We theoretically propose a symmetric encryption scheme based on Restricted Boltzmann Machines that functions as a probabilistic Enigma device, encoding information in the marginal distributions of visible states while utilizing bias permutations as cryptographic keys. Theoretical analysis reveals...

Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs

As Artificial Intelligence AI systems, particularly those based on machine learning ML, become integral to high-stakes applications, their probabilistic and opaque nature poses significant challenges to traditional verification and validation methods. These challenges are exacerbated in regulated...

Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security

Federated learning FL enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been...