Progressive experiments exploring decentralized software technologies for privacy adoption at scale.
How do we pay for privacy?
If you haven’t read Julia Angwin’s excellent profile of GnuPG’s lead developer Werner Koch, now would be a great time to check it out. Koch, who single-handedly wrote GnuPG in 1…
A data minimization model for embedding privacy into software systems
Modern software systems (social networking, banking and shopping applications) are becoming increasingly dependent on our data. These systems need data to provide various economic and social benefits to users as well as businesses. However, the extensive use of personal data in systems poses a threat to user privacy. Therefore, privacy laws expect software systems to practice Data Minimization (DM), to minimize data in software systems. This has put software developers in a dilemma to minimize user data to provide user privacy and maximize user data for enhanced system functionality. Following the design science research approach, in this research we propose and evaluate a methodology that enables developers to make their decisions to minimize user data in software systems through understanding data. The methodology encourage developers to think of the ways they would use data in a system design focusing on the storage and sharing of data. Developers in the three experiments conducted to evaluate the methodology agreed that it enables them to think of the ways they use data in system designs and it helps them to make decisions to minimize using data in a system design. Developers also showed positive intention to use the proposed methodology within system development activities.
VaultGemma: The world's most capable differentially private LLM
Amer Sinha, Software Engineer, and Ryan McKenna, Research Scientist, Google Research
15 Open-Source Tools for Digital Sovereignty (2026) | Comparisons & Alternatives | Vucense
Own your digital stack. The 15 best open-source tools for privacy, security, and full control over your data — reviewed and ranked for 2026.
Measuring the Privacy Experience
How do you know that a product respects your privacy – other than by wading through the fine print? We’ve created a framework to measure the way people actually experience privacy in tech products.
Decentralizability
What makes software decentralizable? Immutable data, universal IDs, user-controlled keys.
Private data: developing a rubric for success - Paul's Leaflets
What will an effective solution look like?
Privacy and human behavior in the age of information
This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people's uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people's concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.
Tiles: A private, collaborative AI assistant that works for you.
A private, collaborative AI assistant that works for you. Built with local models and AT Protocol.
Tiles: A private, collaborative AI assistant that works for you.
A private, collaborative AI assistant that works for you. Built with local models and AT Protocol.
privacy - bad examples
Everyone knows your location