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

爱范儿
爱范儿
T
The Blog of Author Tim Ferriss
G
Google Developers Blog
博客园_首页
博客园 - 【当耐特】
量子位
S
SegmentFault 最新的问题
B
Blog RSS Feed
酷 壳 – CoolShell
酷 壳 – CoolShell
V
Visual Studio Blog
T
Tailwind CSS Blog
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
Y
Y Combinator Blog
博客园 - 聂微东
The Cloudflare Blog
小众软件
小众软件
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
H
Help Net Security
Jina AI
Jina AI
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
宝玉的分享
宝玉的分享

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
Decentralized Privacy-preserving Timed Execution in Block...
Chao Li, Balaji Palanisamy · 2019-02-15 · via cs.CR updates on arXiv.org

In the age of Big Data, enabling task scheduling while protecting users' privacy is critical for various decentralized applications in blockchain-based smart contract platforms. Such a privacy-preserving task scheduler requires the task input data to be secretly maintained until a prescribed task execution time and be automatically recorded into the blockchain to enabling the execution of the task at the execution time, even if the user goes offline. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust and involve a single point of control. This paper presents decentralized techniques for supporting privacy-preserving task scheduling using smart contracts in Ethereum blockchain networks. We design a privacy-preserving task scheduling protocol that is managed by a manager smart contract. The protocol requires a user to schedule a task by deploying a proxy smart contract maintaining the non-sensitive information of the task while creating decentralized secret trust and selecting trustees from the network to maintain the sensitive information of the task. With security techniques including secret sharing and layered encryption as well as security deposit paid by trustees as economic deterrence, the protocol can protect the sensitive information against possible attacks including some trustees destroying the sensitive information (drop attack) or secretly releasing the sensitive information before the execution time (release-ahead attack). We demonstrate the attack-resilience of the proposed protocol through rigorous analysis.Our implementation and experimental evaluation on the Ethereum official test network demonstrate the low monetary cost and the low time overhead associated with the proposed approach.