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

aimingoo的专栏
aimingoo的专栏
宝玉的分享
宝玉的分享
博客园 - 【当耐特】
博客园 - 司徒正美
L
LangChain Blog
有赞技术团队
有赞技术团队
大猫的无限游戏
大猫的无限游戏
Stack Overflow Blog
Stack Overflow Blog
Engineering at Meta
Engineering at Meta
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
I
InfoQ
博客园 - 叶小钗
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
月光博客
月光博客
量子位
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
博客园_首页
Last Week in AI
Last Week in AI
人人都是产品经理
人人都是产品经理
Google DeepMind News
Google DeepMind News
云风的 BLOG
云风的 BLOG
D
DataBreaches.Net

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
A Privacy Measure Turned Upside Down? Investigating the U...
Stephan Wiefling, Marian Hönscheid, Luigi Lo Iacono · 2024-05-22 · via cs.SI updates on arXiv.org

HTTP client hints are a set of standardized HTTP request headers designed to modernize and potentially replace the traditional user agent string. While the user agent string exposes a wide range of information about the client's browser and device, client hints provide a controlled and structured approach for clients to selectively disclose their capabilities and preferences to servers. Essentially, client hints aim at more effective and privacy-friendly disclosure of browser or client properties than the user agent string. We present a first long-term study of the use of HTTP client hints in the wild. We found that despite being implemented in almost all web browsers, server-side usage of client hints remains generally low. However, in the context of third-party websites, which are often linked to trackers, the adoption rate is significantly higher. This is concerning because client hints allow the retrieval of more data from the client than the user agent string provides, and there are currently no mechanisms for users to detect or control this potential data leakage. Our work provides valuable insights for web users, browser vendors, and researchers by exposing potential privacy violations via client hints and providing help in developing remediation strategies as well as further research.