CSV Data Anonymizer

CSV Data Anonymizer is a local-first desktop app for transforming sensitive CSV data before sharing, testing, demos, or support work. It detects likely personal data, previews replacements, and writes a new CSV while preserving the original structure.

All normal CSV processing runs locally in Rust. Optional local LLM replacement also runs on your machine through Ollama.

What It Does

• Detects common sensitive columns: emails, names, phone numbers, UUIDs, timestamps, numeric IDs, addresses, postal codes, IPs, URLs, MAC addresses, tax IDs, and more.
• Auto-selects high and medium risk columns while still letting you choose exactly which columns to transform.
• Shows a preview before writing output.
• Streams large CSV files instead of loading the whole file into memory.
• Keeps repeated source values consistent within a run.
• Supports repeatable replacements with a private seed, useful when multiple files need matching pseudonyms.
• Offers optional Smart replacement with a local LLM for selected columns.
• Produces a privacy report with transformed column counts, reused values, token counts, Local AI replacement counts, and fallbacks.

Local LLM Smart Replacement

Smart replacement is optional and off by default. It is designed for columns where rule-based masking is too mechanical and you want more realistic fake values.

The first implementation uses:

• Ollama running on localhost
• gemma3:4b as the lightweight default model
• In-app status checks, setup link, model download, progress, and cancel controls

Usage:

1. Install or start Ollama.
2. In CSV Anonymizer, enable Local AI in the configuration panel.
3. Download gemma3:4b from the app if it is not already available.
4. Select Smart replacement (Local AI) for the columns that should use the model.
5. Review the preview, then run the anonymization.

The app batches unique values per selected column, asks the local model for realistic fake replacements, validates the response, reuses accepted replacements for repeated source values, and falls back to rule-based pseudonymization when the model output is missing or unsafe.

Model weights and local runtime binaries are not bundled in the repository or desktop release. The first model download uses network access through Ollama. CSV values selected for Smart replacement are sent only to the configured local Ollama endpoint.

Privacy Boundary

The app reduces exposure by masking, pseudonymizing, tokenizing, or locally replacing selected values. It does not currently claim formal anonymization guarantees such as k-anonymity, l-diversity, t-closeness, differential privacy, or synthetic data generation.

Treat output as lower-risk transformed data, not guaranteed anonymous data. Review previews and privacy reports before sharing generated files.

Strategies

Strategy	Use
Mask	Replace values with simple masked output.
Pseudonymize	Generate readable or shape-preserving fake values.
Tokenize	Replace values with stable opaque tok_... tokens.
Smart replacement (Local AI)	Use a local LLM through Ollama for more realistic fake replacements.
Pass-through	Leave values unchanged.

Examples of format preservation include email domains, UUID shape, timestamp precision, numeric width and decimals, phone separators, and full-name token count.

Install

Download desktop builds from GitHub Releases.

macOS:

• Download the .dmg for your Mac.
• Use aarch64 for Apple Silicon and x64 for Intel.
• Drag CSV Anonymizer into Applications.

Linux:

• Download the .AppImage, .deb, or .rpm from the latest release.
• Debian/Ubuntu users can enable the signed APT repository:

bash <(curl -fsSL https://ddv1982.github.io/csv-data-anonymizer/install-apt-repo.sh)
sudo apt update
sudo apt install csv-anonymizer

After the repository is enabled, normal sudo apt update and sudo apt upgrade runs handle updates.

Development

Requirements:

• Rust stable
• Node.js 22.13 or newer
• Frontend dependencies from frontend/package-lock.json

Setup:

npm ci --prefix frontend

Run the desktop app:

npm run tauri:dev

Useful checks:

npm run frontend:typecheck
npm run frontend:build
npm run frontend:audit
cargo fmt --all --check
cargo test --workspace
cargo clippy --workspace --all-targets -- -D warnings
node scripts/rust-smoke.mjs
node scripts/check-release-metadata.mjs

Project Layout

• frontend - React/Vite desktop UI.
• src-tauri - Tauri shell, app settings, commands, background jobs, and Ollama integration.
• crates/csv-anonymizer-core - CSV detection, preview, transformation, reporting, and tests.
• crates/csv-anonymizer-app - lightweight CLI smoke harness for the shared core.
• build - package metadata, icons, and platform assets.
• scripts - release, packaging, metadata, APT, and smoke-test tooling.

Release steps and signing requirements are documented in docs/releasing.md.