Maximizing iPhone Privacy: Essential Settings to Enable, Disable, and Verify Immediately

Top 10 Secure DNS Servers for Privacy, Threat Blocking and Ad Filtering in 2026

Public DNS resolvers differ significantly in their ability to block malicious domains, filter advertising and tracking, respect user privacy, and support modern encryption protocols. The guide evaluates ten services including Quad9, Cloudflare, AdGuard DNS, NextDNS, Control D, CleanBrowsing, Mullvad DNS, DNS4EU, Google Public DNS and DNS.SB, providing IPv4 addresses, DoH and DoT endpoints, and recommended use cases. Key distinctions include Quad9’s focus on phishing and malware blocking without logging, Mullvad’s strict no-logs policy requiring encrypted protocols only, and DNS4EU’s EU-based infrastructure with mandatory court-ordered blocks. The article explains why DNSSEC and encryption alone are insufficient and outlines four practical selection criteria: threat filtering quality, data-handling practices, encryption support, and network stability. Detailed configuration instructions cover Windows 11, Android, iOS, and home routers, along with verification methods and warnings against mixing providers with conflicting policies.

WebRTC IP Leaks Explained: Why Disabling the Protocol Harms Anonymity in Anti-Detect Browsers

The article from Aurorium details how WebRTC technology can leak real IP addresses even when users route traffic through proxies. It explains the peer-to-peer mechanics of WebRTC, the role of STUN servers in generating ICE candidates, and why standard TCP proxies fail to handle UDP traffic. Disabling WebRTC via flags or JavaScript extensions creates detectable anomalies that anti-fraud systems flag as non-standard environments. The recommended approach involves native emulation inside the browser kernel so that STUN requests return proxy IPs while preserving full API integrity across workers. Additional leaks through local network addresses, mDNS candidates, and IPv6 are also covered with practical verification methods using tools like BrowserLeaks and CreepJS.

Docx Anonymizer: Open-Source Chrome Extension Uses Fine-Tuned rubert-tiny2 to Anonymize Personal Data Before AI Uploads

A Russian developer has released Docx Anonymizer, a browser extension that automatically detects and replaces personal data in documents using a lightweight NER model before they reach public AI services such as ChatGPT. The tool was created to help organizations comply with Russia’s 152-FZ personal data protection law while avoiding the risks of uploading contracts and other sensitive files to cloud-based LLMs. It runs entirely locally via ONNX Runtime and WebAssembly, requires no network calls, and supports any AI chat interface. The underlying model, rubert-tiny2, was further trained through knowledge distillation on WikiANN, factRuEval, and synthetic business documents, reaching strong F1 scores for person and organization entities after int8 quantization. The extension intercepts text on the fly, performs named-entity recognition in under two seconds for typical contracts, and replaces detected entities with placeholders. The project is fully open source and designed for minimal user friction, addressing the growing problem of employees inadvertently leaking corporate and personal data to public AI platforms.