Data & Research

A structured benchmark comparison of zero-knowledge proof systems including Groth16, PLONK, STARK, Halo 2, Nova, and Binius. Covers proving time, verification time, proof size, setup requirements, and suitability for privacy-preserving AI, authentication, and cloud applications.

A forensic analysis of VPN provider no-log claims versus documented evidence from court cases, security audits, and data breaches. Covers NordVPN, ExpressVPN, Surfshark, Mullvad, ProtonVPN, PIA, and others with jurisdiction mapping and trust verification data.

Market sizing data and projections for the privacy technology sector from 2021 through 2030, covering privacy-enhancing technologies, confidential computing, data governance tools, and zero-knowledge infrastructure. Segment-level analysis with revenue data and growth rates.

A structured tracker of major privacy regulation enforcement actions globally, including GDPR fines, CCPA actions, FADP enforcement, and AI-specific regulatory precedents. Covers the largest fines, landmark rulings, and their implications for AI privacy architecture.

A technical audit of password manager security architectures, documented breaches, encryption implementations, and trust models. Covers 1Password, Bitwarden, KeePass, LastPass, Dashlane, Proton Pass, and others with vault encryption analysis and breach impact assessment.

A technical comparison of messaging application encryption protocols, metadata exposure, server architecture, and trust models. Covers Signal, WhatsApp, Telegram, iMessage, Matrix/Element, Session, Briar, Wire, and Threema with protocol-level detail and metadata analysis.

A scored comparison matrix ranking major LLM providers across 12 privacy dimensions including data retention, training consent, encryption, jurisdiction, and transparency. Methodology, raw scores, and analysis for OpenAI, Anthropic, Google, Meta, Mistral, Cohere, and more.

A structured matrix of data residency and data localization requirements across 40+ jurisdictions, covering mandatory localization, conditional transfer rules, sector-specific requirements, and the impact on AI data processing and cloud architecture decisions.

A structured comparison of encryption algorithms including AES-256-GCM, RSA, ECC, ChaCha20-Poly1305, and post-quantum candidates (CRYSTALS-Kyber, CRYSTALS-Dilithium). Benchmarks for throughput, key size, security level, and suitability for AI privacy use cases.

A structured comparison of email provider privacy architectures, encryption implementations, jurisdiction exposure, and data access policies. Covers Gmail, Outlook, ProtonMail, Tutanota (Tuta), Fastmail, Posteo, Mailfence, and others with metadata analysis and threat modeling.

A comprehensive audit of data collection practices across developer tools including IDEs, CI/CD platforms, code assistants, package managers, and cloud development environments. Documents what telemetry is collected, where code is transmitted, and the privacy implications for proprietary and sensitive codebases.

A structured breakdown of data breach costs by industry sector in 2026, drawing on IBM Cost of a Data Breach Report data, Ponemon Institute research, and regulatory fine analysis. Includes cost per record, time to containment, and AI-specific breach vectors.

A technical comparison of privacy features across major cryptocurrency networks, including Monero, Zcash, Bitcoin, Ethereum, and newer privacy-focused chains. Covers transaction privacy, address privacy, network-level privacy, and regulatory status with protocol-level detail.

A structured mapping of cloud provider data center locations, legal jurisdictions, government access frameworks, and data residency implications for AWS, Azure, GCP, Cloudflare, and major European and Asian cloud providers.

A comprehensive tracker of major cloud infrastructure outages from 2020-2026, analyzing downtime duration, root causes, affected services, and the overlooked privacy implications of cloud failure modes. Covers AWS, Azure, GCP, Cloudflare, and others.

Quantified data on browser fingerprinting entropy, uniqueness rates, and tracking techniques. Covers canvas fingerprinting, WebGL, AudioContext, font enumeration, and emerging fingerprinting vectors with entropy measurements and anonymity set analysis.

A structured tracker of training data volumes, token counts, and data sources for every major language model from GPT-3 through GPT-5, Claude 3.5, Gemini 2.0, Llama 3.1, and beyond. Includes dataset composition, web scraping volumes, and the privacy implications of scale.

A structured country-by-country comparison of AI privacy regulations across 30+ jurisdictions, covering consent requirements, training data rules, enforcement actions, and cross-border transfer restrictions as of March 2026.

A chronological tracker of every significant AI privacy incident from 2020 through March 2026, including data breaches, training data leaks, prompt exposure events, model memorization disclosures, and regulatory enforcement actions. Structured with dates, affected parties, data types, and resolution status.

Tracking the exponential growth of AI model parameter counts from GPT-2 to current frontier models. Includes parameter counts, training compute, inference costs, and the privacy implications of model scale for data ingestion, memorization, and extraction attacks.