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

Recent Announcements
Recent Announcements
雷峰网
雷峰网
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
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
人人都是产品经理
人人都是产品经理
博客园 - 【当耐特】
量子位
有赞技术团队
有赞技术团队
博客园 - 三生石上(FineUI控件)
博客园 - Franky
M
MIT News - Artificial intelligence
U
Unit 42
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
The Cloudflare Blog
J
Java Code Geeks
V
Visual Studio Blog
Engineering at Meta
Engineering at Meta
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
MyScale Blog
MyScale Blog
T
Tailwind CSS Blog
T
The Blog of Author Tim Ferriss
V
V2EX

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
Reconciling Security and Utility in Next-Generation Epide...
Pierfrancesco Ingo, Nichole Boufford, Ming Cheng Jiang, Rowan Li · 2020-11-17 · via cs.CR updates on arXiv.org

Epidemics like the recent COVID-19 require proactive contact tracing and epidemiological analysis to predict and subsequently contain infection transmissions. The proactive measures require large scale data collection, which simultaneously raise concerns regarding users' privacy. Digital contact tracing systems developed in response to COVID-19 either collected extensive data for effective analytics at the cost of users' privacy or collected minimal data for the sake of user privacy but were ineffective in predicting and mitigating the epidemic risks. We present Silmarillion--in preparation for future epidemics--a system that reconciles user's privacy with rich data collection for higher utility. In Silmarillion, user devices record Bluetooth encounters with beacons installed in strategic locations. The beacons further enrich the encounters with geo-location, location type, and environment conditions at the beacon installation site. This enriched information enables detailed scientific analysis of disease parameters as well as more accurate personalized exposure risk notification. At the same time, Silmarillion provides privacy to all participants and non-participants at the same level as that guaranteed in digital and manual contact tracing. We describe the design of Silmarillion and its communication protocols that ensure user privacy and data security. We also evaluate a prototype of Silmarillion built using low-end IoT boards, showing that the power consumption and user latencies are adequately low for a practical deployment. Finally, we briefly report on a small-scale deployment within a university building as a proof-of-concept.