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
Vercel News
Vercel News
博客园_首页
Y
Y Combinator Blog
美团技术团队
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
阮一峰的网络日志
阮一峰的网络日志
aimingoo的专栏
aimingoo的专栏
H
Hackread – Cybersecurity News, Data Breaches, AI and More
MyScale Blog
MyScale Blog
GbyAI
GbyAI
人人都是产品经理
人人都是产品经理
T
Tailwind CSS Blog
MongoDB | Blog
MongoDB | Blog
D
DataBreaches.Net
博客园 - Franky
Engineering at Meta
Engineering at Meta
量子位
The GitHub Blog
The GitHub Blog
F
Fortinet All Blogs
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
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
酷 壳 – CoolShell
酷 壳 – CoolShell
N
Netflix TechBlog - Medium

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
FairCMS: Cloud Media Sharing with Fair Copyright Protection
Xiangli Xiao, Yushu Zhang, Leo Yu Zhang, Zhongyun Hua, Zhe Liu, · 2021-05-19 · via cs.CR updates on arXiv.org

The onerous media sharing task prompts resource-constrained media owners to seek help from a cloud platform, i.e., storing media contents in the cloud and letting the cloud do the sharing. There are three key security/privacy problems that need to be solved in the cloud media sharing scenario, including data privacy leakage and access control in the cloud, infringement on the owner's copyright, and infringement on the user's rights. In view of the fact that no single technique can solve the above three problems simultaneously, two cloud media sharing schemes are proposed in this paper, named FairCMS-I and FairCMS-II. By cleverly utilizing the proxy re-encryption technique and the asymmetric fingerprinting technique, FairCMS-I and FairCMS-II solve the above three problems with different privacy/efficiency trade-offs. Among them, FairCMS-I focuses more on cloud-side efficiency while FairCMS-II focuses more on the security of the media content, which provides owners with flexibility of choice. In addition, FairCMS-I and FairCMS-II also have advantages over existing cloud media sharing efforts in terms of optional IND-CPA (indistinguishability under chosen-plaintext attack) security and high cloud-side efficiency, as well as exemption from needing a trusted third party. Furthermore, FairCMS-I and FairCMS-II allow owners to reap significant local resource savings and thus can be seen as the privacy-preserving outsourcing of asymmetric fingerprinting. Finally, the feasibility and efficiency of FairCMS-I and FairCMS-II are demonstrated by experiments.