Search Results: PrivacyTech

Unencrypted DNS queries expose your browsing history to ISPs, revealing everything from streaming habits to shopping patterns. Discover how a layered approach using DNS-over-TLS, AdGuard Home, and DNS-over-HTTP/3 can encrypt your data, block trackers, and even boost browsing speeds. This blueprint offers a fail-safe solution for developers and tech enthusiasts to reclaim their digital privacy.

ErnieApp introduces its Privacy Knowledge Manager (PKM), a mobile-first platform aiming to demystify and centralize user privacy controls. The tool offers one-click consent management, data deletion, and a unique gamified element to educate users on data practices, positioning itself as a solution to fragmented privacy settings across hundreds of digital services.

Magic Lasso Adblock reveals they could more than double revenue by selling user data like competitors, exposing the stark financial cost of ethical tech principles. Their analysis spotlights the murky economics of data brokerage and Apple's role in incentivizing surveillance. This transparency challenges the industry's quiet trade-off between privacy and profit.

Proton has launched a free, cross-platform Authenticator app, offering end-to-end encryption and open-source transparency to counter ad-supported rivals like Google Authenticator. This move addresses critical gaps in two-factor authentication, including vendor lock-in and surveillance risks, empowering users with secure, portable TOTP management. The app’s emphasis on privacy and interoperability signals a shift toward user-centric security in an era of escalating cyber threats.

Shynet, an open-source analytics platform, is gaining traction for its unique approach: tracking without cookies or JavaScript. Built with Django and PostgreSQL, it offers self-hosting capabilities and prioritizes user privacy, presenting a compelling option for developers seeking ethical web analytics.

A new Chrome extension called Grammit is challenging established grammar tools like Grammarly by performing all AI-powered writing corrections directly on users' devices. This approach eliminates cloud data transmission, addressing critical privacy concerns for professionals handling sensitive content. The local-processing model could signal a broader shift in how AI writing assistants balance functionality with data security.