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

Stack Overflow Blog
Stack Overflow Blog
S
SegmentFault 最新的问题
大猫的无限游戏
大猫的无限游戏
The GitHub Blog
The GitHub Blog
M
MIT News - Artificial intelligence
T
Tailwind CSS Blog
aimingoo的专栏
aimingoo的专栏
Last Week in AI
Last Week in AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
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
罗磊的独立博客
H
Help Net Security
Engineering at Meta
Engineering at Meta
Microsoft Security Blog
Microsoft Security Blog
阮一峰的网络日志
阮一峰的网络日志
J
Java Code Geeks
T
The Blog of Author Tim Ferriss
Hugging Face - Blog
Hugging Face - Blog
C
Check Point Blog
F
Fortinet All Blogs
腾讯CDC
博客园 - Franky
WordPress大学
WordPress大学
U
Unit 42

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
WhatsApp security and role of metadata in preserving privacy
Nidhi Rastogi, James Hendler · 2017-01-24 · via cs.SI updates on arXiv.org

WhatsApp messenger is arguably the most popular mobile app available on all smart-phones. Over one billion people worldwide for free messaging, calling, and media sharing use it. In April 2016, WhatsApp switched to a default end-to-end encrypted service. This means that all messages (SMS), phone calls, videos, audios, and any other form of information exchanged cannot be read by any unauthorized entity since WhatsApp. In this paper we analyze the WhatsApp messaging platform and critique its security architecture along with a focus on its privacy preservation mechanisms. We report that the Signal Protocol, which forms the basis of WhatsApp end-to-end encryption, does offer protection against forward secrecy, and MITM to a large extent. Finally, we argue that simply encrypting the end-to-end channel cannot preserve privacy. The metadata can reveal just enough information to show connections between people, their patterns, and personal information. This paper elaborates on the security architecture of WhatsApp and performs an analysis on the various protocols used. This enlightens us on the status quo of the app security and what further measures can be used to fill existing gaps without compromising the usability. We start by describing the following (i) important concepts that need to be understood to properly understand security, (ii) the security architecture, (iii) security evaluation, (iv) followed by a summary of our work. Some of the important concepts that we cover in this paper before evaluating the architecture are - end-to-end encryption (E2EE), signal protocol, and curve25519. The description of the security architecture covers key management, end-to-end encryption in WhatsApp, Authentication Mechanism, Message Exchange, and finally the security evaluation. We then cover importance of metadata and role it plays in conserving privacy with respect to whatsapp.