PII GUI is a Tauri 2 desktop app (React 19 + TypeScript frontend, Rust backend) for local-first PII detection and redaction. Detection runs on-device with regex rules or quantized ONNX models; the only network access is the optional one-time model download.

Example

PII GUI supports two local workflows.

Text <-> PII	Redact PDF
Text -> PII: detect names, emails, phone numbers, URLs, dates, IDs, and secrets before exporting a redacted copy. PII -> Text: restore reviewed placeholders back into readable text when you need a reversible local review workflow.	Burn approved redactions into exported PDFs so hidden text is not recoverable from the output file.

Features

• Local inference only — PII detection runs entirely on-device. The only network access is the optional one-time model download from Hugging Face.
• PDF, Markdown, and plain-text input — PDFs are parsed with pdf.js, preserving per-character positions so detections are highlighted directly on the rendered page.
• Custom rules — add your own regex or exact-match filters on top of any backend.
• Review before redacting — toggle individual matches on or off in the workbench before export.
• True PDF redaction — each page that carries an approved redaction is re-rendered and burned into the export as an image with pdf-lib, so the redacted text is not recoverable from the output file. Pages without redactions are copied through with their text layer intact, which keeps them searchable and the export small.
• Task history and persistence — tabs, custom rules, and filter results survive restarts via a local SQLite database and on-disk result files. Settings shows what is stored locally and can delete it.
• Long-document support — input is split into token-bounded, page-aware chunks and processed through a task queue.
• Localized UI — English, Korean, and Japanese.

Detection Backends

Backend	Best for
Regex (built-in)	Instant baseline detection of emails, phones, URLs, dates, account numbers, and secrets
OpenAI Privacy Filter	Long English documents and broad privacy-taxonomy detection
BardsAI EU PII	European-language text where names, addresses, and ID-like entities matter

Detection taxonomy

Matches are labeled with a fixed privacy taxonomy:

account_number · private_address · private_email · private_person · private_phone · private_url · private_date · secret

Benchmarks

OpenAI Privacy Filter ONNX inference was measured locally on an Apple M1 Max using ONNX Runtime CPUExecutionProvider. The benchmark uses 1024-token chunks, strips speaker-name lines, and reports the median of 3 measured repeats after 1 warmup run.

Model	Quantization	1k latency	1k throughput	8k latency	8k throughput
openai/privacy-filter	fp32	620.52 ms	1,664 tok/s	4,893.86 ms	1,689 tok/s
openai/privacy-filter	fp16	654.56 ms	1,578 tok/s	5,430.17 ms	1,521 tok/s
openai/privacy-filter	q4	582.13 ms	1,776 tok/s	4,635.39 ms	1,784 tok/s
openai/privacy-filter	q4f16	648.10 ms	1,594 tok/s	5,261.56 ms	1,570 tok/s
openai/privacy-filter	int8 dynamic	573.94 ms	1,801 tok/s	4,594.95 ms	1,800 tok/s

On this Apple M1 Max CPU path, the int8 dynamic and q4 variants were the fastest. fp16 and q4f16 were slower than fp32 with CPUExecutionProvider.

Requirements

• Node.js 24+
• pnpm
• Rust and Cargo
• Tauri v2 platform prerequisites for your OS

Setup

Download the latest installer for macOS, Windows, or Linux from the Releases page.

On first launch, the onboarding flow lets you pick a default backend. Regex works immediately; the ONNX models are optional downloads (fetched from Hugging Face into the app data directory, and removable at any time from Settings).

Install from source:

cd tauri
pnpm install

Local release signing values are optional for development. If you need updater signing locally, copy the environment template and fill in your own key:

cp .env.example .env

How it works

Document (PDF / md / txt)
  → text extraction (pdf.js, per-character boxes for PDFs)
  → token-bounded, page-aware chunking
  → task queue → Rust `redact_text` command
  → regex / ONNX inference (ort + tokenizers)
  → matches + redacted text
  → review & toggle matches in the UI
  → export (burned-in PDF redaction or redacted text)

The frontend (React) handles document parsing, chunking, review, and export. The Rust backend (src-tauri/) owns the detection engines, model lifecycle (download / verify / delete), and file I/O. Everything the app stores for itself — the SQLite database, the PDF redaction result files, and the downloaded models — stays inside the Tauri app data directory, and an export writes only to the path you pick in the native save dialog.

Development

Run from source

cd tauri
pnpm install
pnpm tauri dev

Build

cd tauri
pnpm tauri build

Tests

cd tauri
pnpm test:unit              # frontend unit tests (vitest)
pnpm test:unit:coverage     # unit tests with a coverage report
pnpm lint                   # eslint
pnpm build                  # typecheck + frontend build

cd src-tauri
cargo test                  # Rust backend tests

Roadmap

• Local regex detection and review workflow
• Optional ONNX backend wiring for OpenAI Privacy Filter and BardsAI EU PII
• Burned-in PDF redaction export
• Local tab, custom-rule, and result persistence
• Broader import/export QA for large PDFs and multilingual documents
• Accessibility and keyboard-only review pass
• Integration with coding agents (Codex, Claude Code, Cursor)

Project structure

tauri/                      # the desktop app
  src/                      # React frontend
    App.tsx                 # orchestrator: tabs, routing, workbench
    components/             # PDF preview, shadcn/Radix UI primitives
    lib/
      pdf-document.ts       # pdf.js text + char-box extraction
      pii-text-chunks.ts    # token-bounded chunking
      pii-task-queue.ts     # detection task queue
      redaction-policy.ts   # match merge/select/restore logic
      pdf-redacted-export.ts# burned-in PDF redaction export
      app-persistence.ts    # SQLite + result-file persistence
      i18n.ts               # en / ko / ja UI copy
  src-tauri/                # Rust backend
    src/lib.rs              # Tauri commands: redact_text, model lifecycle, file I/O
    src/redact_engine.rs    # regex / ONNX / BardsAI detection backends
docs/assets/                # README thumbnail and screenshot assets

Contributing

Contributions are welcome!

• Bug reports & feature requests — open an issue with steps to reproduce or a short description of the use case.
• Pull requests — keep changes small and focused. Before submitting, run the checks for the area you touched:
  • Frontend: pnpm test:unit and pnpm build from tauri/
  • Rust backend: cargo test from tauri/src-tauri/
• Detection quality — false positives/negatives are especially useful to report; include the backend (regex / Privacy Filter / BardsAI) and a minimal, PII-free sample that reproduces the issue.
• Benchmarks — keep local benchmark scripts and outputs out of commits; /benchmarks/ is ignored.

License

PII GUI is licensed under the GNU Affero General Public License v3.0.

Acknowledgements

PII GUI builds on several open-source projects and model releases:

• pdf.js and pdf-lib for PDF parsing and export.
• ONNX Runtime and tokenizers for local model inference.
• OpenAI Privacy Filter and BardsAI EU PII for optional local PII detection models.

Verification

Run the smallest check that proves the change, then widen as needed:

cd tauri && pnpm test:unit
cd tauri && pnpm build
cd tauri/src-tauri && cargo check
git diff --check

For packaging, run pnpm tauri build on the target platform before making release claims.