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Fingerprinting toolbox that allows embedding and extracting fingerprints into the relational data

License: Apache License 2.0

Python 2.89% Jupyter Notebook 97.11%

fingerprinting-toolbox's Introduction

Fingerprinting Relational Data

Fingerprinting toolbox is a library that allows embedding and extracting fingerprints into the relational data.

Usage

You can use the toolbox by cloning this repository.

$ git clone https://github.com/tanjascats/fingerprinting-toolbox.git

Fingerprint embedding (insertion)

For fingerprint insertion, we can define the scheme with the parameter gamma and bit-length of a fingerprint. The number of modified rows in the data will then be approx. #rows/gamma (TIP: use gamma to control the amount of modifications in the data).

scheme = Universal(gamma=2, fingerprint_bit_length=64)

After the scheme is initialized, we can embedd the fingerprint using our (owner's) secret key and specifying recipient's ID:

fingerprinted_data = scheme.insertion("my_data.csv", secret_key=12345678, recipient_id=0)

Fingerprint extraction (detection)

For the fingerprint extraction, we provide the suspicious data and the secret key used for embedding:

suspect = scheme.detection("suspicious_data.csv", secret_key=12345678)

For more examples and detailed explanations check out the notebook example.ipynb.

Support & Contribution

The toolbox is in its early stage, but actively developing, so you can either:

  • Report the issues at Issues or
  • Contact me by email: [email protected] for questions, suggestions or issues
  • Pull requests are welcome

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