Privacy

Private computation is presented as a way to return value or control over personal data to individuals. The privacy analysis uses empirical output perturbation rather than formal differential privacy. The system does not provide formal epsilon-delta differential privacy because neural feature-extractor sensitivity is estimated empirically rather than tightly bounded. The paper describes privacy as empirical mitigation rather than a formal guarantee. The article does not provide a complete governance framework for separating legitimate privacy from harmful use. During onboarding, Gaussian noise is added to trait embeddings with selected scale sigma 25.3 per dimension. Powerful privacy technologies can protect legitimate rights but may also enable harmful activity. Adding noise with sigma 25.3 made MIA-AUC near random but reduced agitation prediction performance.