veildata 0.1.1

Framework for masking and unmasking PII.

🕶️ VeilData

A lightweight framework for masking and unmasking Personally Identifiable Information (PII).

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🧠 Why VeilData

Modern AI systems touch sensitive data every day.
VeilData makes it easy to redact, anonymize, and later restore information—using the same composable design you love from PyTorch.

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🚀 Quick Start

Installation

From PyPI

pip install veildata

Run from Docker

docker build -t veildata .
alias veildata="docker run --rm -v \$(pwd):/app veildata"
veildata mask data/input.csv --out data/redacted.csv

Running in Docker

docker build -t veildata .
docker run -it ghcr.io/veildata/veildata:latest

For Development

git clone https://github.com/VeilData/veildata.git
cd veildata
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv
uv sync

Quickstart Guide

Mark sensitive data

veildata mask input.txt --out masked.txt

Reveal previously mask data

veildata unmask masked.txt --store mappings.json --out revealed.txt

** Using Docker**

docker run --rm -v $(pwd):/app veildata mask input.txt --out masked.txt

Examples

Regex-based Masking

from veildata import Compose, RegexMasker, TokenStore

# Create a shared TokenStore for reversible masking
store = TokenStore()

# Define your masking pipeline with the shared store
masker = Compose([
    RegexMasker(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}", store=store),  # email
    RegexMasker(r"\b\d{3}-\d{3}-\d{4}\b", store=store),                           # phone
])

text = "Contact John at [e-mail] or call 123-456-7890."

# --- Mask the data ---
masked_text = masker(text)
print(masked_text)
# -> Contact John at [REDACTED_1] or call [REDACTED_2].

# --- Unmask it later ---
unmasked_text = store.unmask(masked_text)
print(unmasked_text)
# -> Contact John at [e-mail] or call 123-456-7890.

spaCy Named Entity Recognition

# Requires `pip install veildata[spacy]`
from veildata.maskers.ner_spacy import SpacyNERMasker
from veildata import TokenStore

# Shared token store for reversible unmasking
store = TokenStore()

masker = SpacyNERMasker(
    entities=["PERSON", "ORG"],
    store=store
)

text = "John works at OpenAI in San Francisco."

# --- Mask automatically and track mappings ---
masked = masker(text)
print(masked)
# -> [REDACTED_1] works at [REDACTED_2] in San Francisco.

# --- Unmask using the same store ---
unmasked = store.unmask(masked)
print(unmasked)
# -> John works at OpenAI in San Francisco.

BERT-based Masking

from veildata.bert_masker import BERTNERMasker

masker = BERTNERMasker(model_name="dslim/bert-base-NER")
text = "Email Jane at [e-mail]"
print(masker(text))
# -> Email [REDACTED] at [REDACTED]

🛠️ Continuous Integration

• CI: .github/workflows/ci.yml runs linting, formatting, build, and tests on every push or PR.
• Publish: .github/workflows/publish.yml auto-publishes to PyPI when a new v* tag or release is created.