privacy-feature-design

To discover privacy features in an existing codebase, you can use codebase analysis to identify trust and anonymity capabilities. This process scans your code to find existing implementations like data export or local-first options that strengthen user trust.

An anonymity model in software feature design is a framework ensuring features do not erode user anonymity. It guides feature discovery and specification by aligning codebase capabilities with strict privacy requirements and threat modeling.

Writing a privacy feature specification involves documenting mini-specs for chosen features that detail their impact and threat-model fit. This requires analyzing the codebase to ensure specified features align with your anonymity model.

Yes, codebase analysis can be used to find zero-knowledge protocol implementations. By analyzing the code, you can identify these high-value privacy features and specify their alignment with your anonymity model.

No, this privacy feature discovery process focuses strictly on discovery and specification without implementation. It identifies high-value privacy features in the codebase and creates mini-specs rather than writing the actual code.

To specify high-value privacy features, you need codebase access and knowledge of your anonymity model. This allows you to perform code analysis and threat modeling to document features with their impact and threat-model fit.