惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
云风的 BLOG
云风的 BLOG
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
D
Docker
H
Help Net Security
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
Tailwind CSS Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
月光博客
月光博客
C
Check Point Blog
S
SegmentFault 最新的问题
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
M
MIT News - Artificial intelligence
B
Blog RSS Feed
MyScale Blog
MyScale Blog
大猫的无限游戏
大猫的无限游戏
Hugging Face - Blog
Hugging Face - Blog
腾讯CDC
美团技术团队
I
InfoQ
Blog — PlanetScale
Blog — PlanetScale

cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
The evolving ecosystem of COVID-19 contact tracing applic...
Benjamin Levy, Matthew Stewart · 2021-03-19 · via cs.CR updates on arXiv.org

Since the outbreak of the novel coronavirus, COVID-19, there has been increased interest in the use of digital contact tracing as a means of stopping chains of viral transmission, provoking alarm from privacy advocates. Concerning the ethics of this technology, recent studies have predominantly focused on (1) the formation of guidelines for ethical contact tracing, (2) the analysis of specific implementations, or (3) the review of a select number of contact tracing applications and their relevant privacy or ethical implications. In this study, we provide a comprehensive survey of the evolving ecosystem of COVID-19 tracing applications, examining 152 contact tracing applications and assessing the extent to which they comply with existing guidelines for ethical contact tracing. The assessed criteria cover areas including data collection and storage, transparency and consent, and whether the implementation is open source. We find that although many apps released early in the pandemic fell short of best practices, apps released more recently, following the publication of the Apple/Google exposure notification protocol, have tended to be more closely aligned with ethical contact tracing principles. This dataset will be publicly available and may be updated as the pandemic continues.