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

Hugging Face - Blog
Hugging Face - Blog
Vercel News
Vercel News
C
Check Point Blog
G
Google Developers Blog
博客园 - 司徒正美
量子位
Engineering at Meta
Engineering at Meta
S
SegmentFault 最新的问题
Google DeepMind News
Google DeepMind News
F
Fortinet All Blogs
A
About on SuperTechFans
美团技术团队
D
DataBreaches.Net
Stack Overflow Blog
Stack Overflow Blog
Jina AI
Jina AI
Y
Y Combinator Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
J
Java Code Geeks
MongoDB | Blog
MongoDB | Blog
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The Cloudflare Blog
U
Unit 42

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
Minoritised Ethnic People's Security and Privacy Concerns...
Aunam Quyoum, Mark Wong, Sebati Ghosh, Siamak F. Shahandashti · 2025-06-06 · via cs.CR updates on arXiv.org

Minoritised ethnic people are marginalised in society, and therefore at a higher risk of adverse online harms, including those arising from the loss of security and privacy of personal data. Despite this, there has been very little research focused on minoritised ethnic people's security and privacy concerns, attitudes, and behaviours. In this work, we provide the results of one of the first studies in this regard. We explore minoritised ethnic people's experiences of using essential online services across three sectors: health, social housing, and energy, their security and privacy-related concerns, and responses towards these services. We conducted a thematic analysis of 44 semi-structured interviews with people of various reported minoritised ethnicities in the UK. Privacy concerns and lack of control over personal data emerged as a major theme, with many interviewees considering privacy as their most significant concern when using online services. Several creative tactics to exercise some agency were reported, including selective and inconsistent disclosure of personal data. A core concern about how data may be used was driven by a fear of repercussions, including penalisation and discrimination, influenced by prior experiences of institutional and online racism. The increased concern and potential for harm resulted in minoritised ethnic people grappling with a higher-stakes dilemma of whether to disclose personal information online or not. Furthermore, trust in institutions, or lack thereof, was found to be embedded throughout as a basis for adapting behaviour. We draw on our results to provide lessons learned for the design of more inclusive, marginalisation-aware, and privacy-preserving online services.