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

博客园_首页
Y
Y Combinator Blog
Engineering at Meta
Engineering at Meta
D
Docker
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
大猫的无限游戏
大猫的无限游戏
腾讯CDC
P
Proofpoint News Feed
A
About on SuperTechFans
WordPress大学
WordPress大学
Stack Overflow Blog
Stack Overflow Blog
Google DeepMind News
Google DeepMind News
C
Check Point Blog
Microsoft Security Blog
Microsoft Security Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
L
LangChain Blog
MyScale Blog
MyScale Blog
博客园 - 三生石上(FineUI控件)
Hugging Face - Blog
Hugging Face - Blog
Microsoft Azure Blog
Microsoft Azure Blog
N
Netflix TechBlog - Medium
G
Google Developers Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

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
Attribute-Based Encryption With Payable Outsourced Decryp...
Dongliang Cai, Borui Chen, Liang Zhang, Kexin Li, Haibin Kan · 2024-11-06 · via cs.CR updates on arXiv.org

Attribute-Based Encryption (ABE) is a promising solution for access control in cloud services. However, the heavy decryption overhead hinders its widespread adoption. A general approach to address this issue is to outsource decryption to decryption cloud service(DCS). Existing schemes have utilized various methods to enable users to verify outsourced results; however, they lack an effective mechanism to achieve exemptibility which enables the honest DCS to escape from wrong claims. And it is impractical to assume that the DCS will provide free services. In this paper, we propose a blockchain-based payable outsourced decryption ABE scheme that achieves both verifiability and exemptibility without adding redundant information to ABE ciphertext. We use zero-knowledge proof to verify outsourced results on blockchain and introduce an optional single-round challenge game under optimistic assumption to address the high cost of proof generation. Moreover, our system achieves fairness and decentralized outsourcing to protect the interests of all parties. Finally, we implement and evaluate our scheme on Ethereum to demonstrate its feasibility and efficiency, the gas usage in attribute numbers from 5 to 60 is 11$\times$ to 140$\times$ in the happy case and 4$\times$ to 55$\times$ in the challenge case lower than the scheme of Ge et al. (TDSC'23).