Valerio Maggio
Valerio Maggio is a Researcher, and Education Lead at Open Mined. He is well versed in open science and research software, and he has been recently awarded a fellowship from the Software Sustainability Institute (profile) focused on developing open teaching modules on Privacy-Preserving Machine Learning. Valerio is also an open-source contributor, and an active member of the Python community, helping with the organisation of many international conferences and community meetups like PyCon Italy, PyData, EuroPython, and EuroSciPy. All his talks, workshop materials and random ramblings are publicly available on his Speaker Deck and GitHub profiles. In his free time, Valerio is a casual Magic: The Gathering wizard 🧙♂️, of course playing a community magic format.
Session
In today's data-driven world, privacy stands as an essential requirements for the ethical and effective practice of data science. Moreover, the implementation of robust privacy guarantees in data analysis not only protects sensitive information, but also unlocks the potential for unprecedented democratisation of models and datasets.
PySyft is a stack of open source tools that is designed to help organisations to securely collaborate with external (untrusted) individuals. By using PySyft, organisations can enable external auditors (e.g. data scientists) to use their assets, such as datasets or models, in order to conduct studies with a specific, known purpose. Data scientists can run their analysis using those assets through PySyft, and without seeing nor obtaining a copy of the assets themselves. We call this process Remote Data Science. PySyft is a framework for Remote Data Science.
In the first part of my talk I will introduce the problem of privacy in Data Science, PETs (Privacy Enhancing Technologies), and OpenMined mission to democratise access to data and information. Afterwards, I will demonstrate how PySyft works, and how it can be used to run a machine learning experiments, with privacy guarantees.