9 matches found
secretflow
简体中文｜English SecretFlow是一个统一的框架，用于保护隐私的数据智能和机器学习。为了实现这个目标，它提供了以下内容： 抽象设备层，包括明文设备和封装了各种密态协议的密态设备。 设备流层，将高阶算法转为设备对象流和DAG。 算法层，使用水平或垂直分区的数据进行数据分析和机器学习。 工作流层，无缝集成数据处理、模型训练和超参调整。 文档 SecretFlow 快速开始 用户指南 API文档 教程 相关项目 Kuscia: 一款基于 K3s 的轻量级隐私计算任务编排框架。 SCQL: 允许多个不信任方在不泄露其私人数据的情况下进行联合分析的系统。 SPU:...
Code-Centric Detection of Vulnerability-Fixing Commits: A Unified Benchmark and Empirical Study
Automated detection of vulnerability-fixing commits VFCs is critical for timely security patch deployment, as advisory databases lag patch releases by a median of 25 days and many fixes never receive advisories. We present a comprehensive evaluation of code language model based VFC detection...
ACIArena: Toward Unified Evaluation for Agent Cascading Injection
Collaboration and information sharing empower Multi-Agent Systems MAS but also introduce a critical security risk known as Agent Cascading Injection ACI. In such attacks, a compromised agent exploits inter-agent trust to propagate malicious instructions, causing cascading failures across the...
SoK: Harmonizing Attack Graphs and Intrusion Detection Systems
Detecting and responding to cyber attacks is increasingly difficult as high-volume, complex network traffic allows threats to remain concealed. While Intrusion Detection Systems IDSs identify anomalous behavior, Attack Graphs AGs serve as the primary threat model for analyzing attacker strategies...
A Unified Evaluation of Learning-Based Similarity Techniques for Malware Detection
Cryptographic digests e.g., MD5, SHA-256 are designed to provide exact identity. Any single-bit change in the input produces a completely different hash, which is ideal for integrity verification but limits their usefulness in many real-world tasks like threat hunting, malware analysis and digita...
SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Systemic Weaknesses
Wi-Fi Channel State Information CSI has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational feasibility. However, the field lacks a consolidated understanding of its security properties, adversarial resilience, and methodological consistency. This...
MUBox: a Critical Evaluation Framework of Deep Machine Unlearning
Recent legal frameworks have mandated the right to be forgotten, obligating the removal of specific data upon user requests. Machine Unlearning has emerged as a promising solution by selectively removing learned information from machine learning models. This paper presents MUBox, a comprehensive...
Fine-Grained Manipulation Attacks to Local Differential Privacy Protocols for Data Streams
Local Differential Privacy LDP enables massive data collection and analysis while protecting end users' privacy against untrusted aggregators. It has been applied to various data types e.g., categorical, numerical, and graph data and application settings e.g., static and streaming. Recent finding...
