privJedAI 0.0.4

An open-source library that builds powerful end-to-end PPRL workflows.

An open-source library that leverages Python’s data science ecosystem to build
powerful end-to-end Entity Resolution workflows.

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privJedAI

📚 Read the Official Documentation

Check out our Read the Docs page for publications, tutorials, and quickstart guides!

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Overview

privJedAI is a python framework, aiming to offer experts and novice users, robust and fast solutions for Privacy Preserving Record Linkage. It is builded using state-of-the-art python frameworks. privJedAI constitutes the sole open-source Link Discovery tool that is capable of exploiting the latest breakthroughs in Deep Learning and NLP techniques, which are publicly available through the Python data science ecosystem. This applies to both blocking and matching, thus ensuring high time efficiency, high scalability as well as high effectiveness, without requiring any labelled instances from the user.

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Key-Features

• Input data-type independent. Both structured and semi-structured data can be processed.
• Various implemented algorithms.
• Easy-to-use.
• Utilizes some of the famous and cutting-edge machine learning packages.
• Offers supervised and un-supervised ML techniques.

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Install

privJedAI has been tested on Linux OS.

PyPI

Install the latest version of pyjedai:

pip install privjedai

More on PyPI.

Git

Set up locally:

git clone https://github.com/AI-team-UoA/privJedAI.git

go to the root directory with cd privJedAI and type:

pip install .

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Tutorials

Open demos are available in:

       

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Dependencies

         

See the full list of dependencies and all versions used, in this file.

Statistics & Info

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Team & Authors

• Lefteris Stetsikas, Research Associate at University of Athens, Greece
• Dimitris Karapiperis, Senior Researcher at International Hellenic University
• George Papadakis, Senior Researcher at University of Athens, Greece
• Manolis Koubarakis, Professor at University of Athens, Greece

Research and development is made under the supervision of Pr. Manolis Koubarakis. This is a research project by the AI-Team of the Department of Informatics and Telecommunications at the University of Athens.

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License

Released under the Apache-2.0 license (see LICENSE.txt).

Copyright © 2026 AI-Team, University of Athens

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Acknowledgements

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This project is being funded in the context of RECITALS that is an HORIZON-Europe project.