Andrei Stoian - Open-source Machine Learning on Encrypted Data | PyData Amsterdam 2024

www.pydata.org

This talk is about data science on encrypted data with Python and Fully Homomorphic Encryption (FHE). FHE is a groundbreaking technology that secures data while preserving its utility, allowing data owners to process it even in its encrypted state. This talk will show how Concrete ML, a Python framework for data science with FHE, makes it easy to convert machine learning models to work with encrypted data, without knowing anything about cryptography.

The talk is addressed to privacy-minded Python developers that want to learn what is possible to build with FHE and how to build privacy and confidentiality features into their software. The talk is informative and assumes some participants have some data science knowledge. A first part will focus on use-cases and a second part on using Concrete ML in conjunction with other Python data processing libraries to implement the use-cases.

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

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