Bidirectional token-classification model for identifiable info
...It can run locally on standard hardware, ensuring that sensitive information never leaves the user’s environment and supporting privacy-first workflows. The system is fine-tunable, enabling adaptation to specific datasets or compliance requirements across industries. It identifies multiple categories of sensitive data such as names, emails, and credentials, replacing them with placeholders to preserve structure.
Lightweight on-device model for private AI text redaction
Rampart is a lightweight, on-device privacy protection model developed by the National Design Studio to detect and redact personally identifiable information (PII) before text leaves a user's device. Rather than relying on server-side filtering, Rampart performs token-level PII detection locally, enabling privacy-preserving AI interactions with minimal latency and without exposing sensitive information to external services. The released model is a 14.7 MB ONNX artifact based on a fine-tuned...