Another Co*cryption Method

Key-dependent Security of Stream Ciphers

The control of the cryptography is more than ever a recurrent issue. As the current international regulation does not apply in the signatory countries, the concept of enforcing backdoors in encryption system is reborn with more strength. This paper deals with a particular class of stream cipher backdoors. This class, under a different form, has been widely used by the industry in the 80s and 90s in the context of the export control rules imposed by the US to the Western countries. We propose here a new system -- called BSEA-2, with a 128-bit secret key -- which is a seemingly minor modificatio…

Skefl：用于安全联邦学习的单密钥同态加密

Skefl: Single-Key Homomorphic Encryption for Secure Federated Learning

针对联邦学习中单密钥同态加密易受服务器与客户端串通攻击的问题，提出Skefl协议，通过密文秘密共享实现安全性与性能的平衡，并给出安全性证明与性能评估。

A Tight High-Order Entropic Quantum Uncertainty Relation With Applications

We derive a new entropic quantum uncertainty relation involving min-entropy. The relation is tight and can be applied in various quantum-cryptographic settings. Protocols for quantum 1-out-of-2 Oblivious Transfer and quantum Bit Commitment are presented and the uncertainty relation is used to prove the security of these protocols in the bounded quantum-storage model according to new strong security definitions. As another application, we consider the realistic setting of Quantum Key Distribution (QKD) against quantum-memory-bounded eavesdroppers. The uncertainty relation allows to prove the se…

跨越数据流：通过多路复用信道中的公共压缩上下文恢复 SSH 明文

Crossing the Streams: SSH Plaintext Recovery via a Common Compression Context in Multiplexed Channels

本研究首次发现SSH信道多路复用共享压缩上下文导致压缩侧信道，提出自适应选择明文攻击，可在低噪声场景下用最多276次猜测恢复8字符秘密。

利用 ChatGPT 的有效侦察技术最大化渗透测试成功率

Maximizing Penetration Testing Success with Effective Reconnaissance Techniques using ChatGPT

本文通过案例研究评估 ChatGPT 在渗透测试侦察阶段收集 IP、域名、网络拓扑和服务等情报的作用，以改进测试规划并发现安全风险。

Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is …