camouflage 1.0.4

Camouflage library, reversible data anonymization

Camouflage 🛡️

Anonymize. Protect. Restore.
Flexible and reversible anonymization for modern Python workflows.

---

Ready to get started?

Install Camouflage with pip:

pip install camouflage

---

✨ What is Camouflage?

Camouflage lets you easily anonymize sensitive data, store reversible mappings, and restore the original dataset when needed — all while being fast, lightweight, and fully customizable.

• 🔥 Anonymize large datasets quickly.
• 🛠️ Add your own plugins easily (your data, your rules).
• 🔄 Reversible by design — restore original values without headaches.
• 🧪 100% test coverage for maximum trust.
• 🏎️ Tested on datasets with over 100,000 rows across 6 columns — handles big data smoothly.

---

📈 How it Works

Camouflage uses a one-to-one mapping to anonymize data. It generates a unique, consistent, and reversible mapping for each value. See Bijection on Wikipedia.

Camouflage guarantees that every anonymized value is unique, consistent, and traceable back — only when you need it.

---

🚀 Quick Start

1️⃣ One-Time Anonymization

from camouflage import anonymize

original_value = "192.168.1.1"

anonymized_value = anonymize("ipv4", original_value)

2️⃣ Reversible Anonymization

from camouflage import anonymize, deanonymize, Transform

original_value = "192.168.1.1"

transform = Transform()

# Anonymize
anonymized_value = anonymize("ipv4", original_value, transform)

# Do something with the anonymized value
# ...

# De-anonymize
deanonymized_value = deanonymize("ipv4", anonymized_value, transform)

3️⃣ Anonymizing a Pandas DataFrame

import pandas as pd
from camouflage import PandasAdapter

df = pd.DataFrame({
    "ip": ["192.168.1.1", "10.0.0.1"],
    "joined_at": [pd.Timestamp("2023-01-01"), pd.Timestamp("2023-02-01")],
    "revenue": [1234.56, 7890.12],
})
# | ip          | joined_at           |   revenue |
# |:------------|:--------------------|----------:|
# | 192.168.1.1 | 2023-01-01 00:00:00 |   1234.56 |
# | 10.0.0.1    | 2023-02-01 00:00:00 |   7890.12 |

mapper = {
    "ip": "ipv4",
    "joined_at": "datetime",
    "revenue": "amount",
}

pd_adapter = PandasAdapter(mapper)

df_safe = pd_adapter.anonymize(df)
# | ip             | joined_at           |   revenue |
# |:---------------|:--------------------|----------:|
# | 137.224.91.30  | 2024-12-05 00:00:00 |   1279.97 |
# | 213.209.12.210 | 2023-06-27 00:00:00 |   5506.58 |

# Do something with the anonymized DataFrame
# ...

# When you want to restore:
original_df = pd_adapter.deanonymize(df_safe)
# | ip          | joined_at           |   revenue |
# |:------------|:--------------------|----------:|
# | 192.168.1.1 | 2023-01-01 00:00:00 |   1234.56 |
# | 10.0.0.1    | 2023-02-01 00:00:00 |   7890.12 |

---

🧩 Extending with Custom Anonymizers

Want to anonymize new types of data? Super easy:

1️⃣ Create your Custom Anonymizers

import random


def anonymize_color(_):  # It is crucial for the anonymizer to accept a single argument.
    return random.choice(['red', 'green', 'blue'])


def anonymize_red_channel(original_hex):
    hex_color = original_hex.lstrip('#')

    green = hex_color[2:4]
    blue = hex_color[4:6]

    random_red = random.randint(0, 255)

    return "#{:02X}{}{}".format(random_red, green, blue)

2️⃣ Register the Anonymizers

from camouflage import register_anonymizer

register_anonymizer('color', anonymize_color)
register_anonymizer('red_channel', anonymize_red_channel)

3️⃣ Use the Custom Anonymizers

from camouflage import anonymize

original_value = "cyan"
anonymized_value = anonymize("color", original_value)

original_hex = "#00FF00"
anonymized_hex = anonymize("red_channel", original_hex)

4️⃣ Or Use the Custom Anonymizers for Pandas

import pandas as pd
from camouflage import PandasAdapter

df = pd.DataFrame({
    "color": ["cyan", "magenta", "yellow"],
    "hex": ["#FF0000", "#00FF00", "#0000FF"],
})
# | color   | hex     |
# |:--------|:--------|
# | cyan    | #FF0000 |
# | magenta | #00FF00 |
# | yellow  | #0000FF |

mapper = {
    "color": "color",
    "hex": "red_channel",
}

pd_adapter = PandasAdapter(mapper)
df_safe = pd_adapter.anonymize(df)
# | color   | hex     |
# |:--------|:--------|
# | green   | #B90000 |
# | blue    | #96FF00 |
# | red     | #FD00FF |

✅ That's it — now you can anonymize columns as "color" or "red_channel" either one-time or in adapters!

---

✅ Quality You Can Trust

• ✅ 100% code coverage (Pytest + Coverage)
• ✅ PEP8 compliant, linted
• ✅ Fast anonymization for datasets of 100,000+ rows
• ✅ Extensible plugin system
• ✅ Tested and battle-ready

---

🧪 Testing

Run tests on your setup with:

pip install pytest
pytest

📜 License

MIT License — do whatever you want, but be cool. ✌️

---

👨‍💻 Made with ❤️ by Developers, for Developers.

Camouflage is built to empower privacy-first applications without slowing you down.

---

🔗 Links

Source Code: https://github.com/data-minder/camouflage
PyPI: https://pypi.org/project/camouflage/