In the first part of this article, you had your first encounter with Differential Privacy and learned why it’s so awesome. In this second part, we’ll present to you three python libraries for implementing Differential Privacy: Difflibpriv, TensorFlow-Privacy and Opacus.

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Does the term Differential Privacy ring a bell? If it doesn’t then you’re in for a treat! The first part of this article provides a quick introduction to the notion of Differential Privacy, a new robust mathematical approach to formulate the privacy guarantee of data related tasks. We will be covering some of its use cases, untangle its mechanisms and key properties and see how it works in practice.

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This blog post is an introduction to the concept of pattern distillation and its link to privacy. It was written by Gijs Barmentlo as part of Data For Good season 8.

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This article attempts to clear up the complex and vast subject of fairness in machine learning. Without being exhaustive, it proposes a certain number of definitions and very useful tools that every Data Scientist must appropriate in order to address this topic.

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So as to meet the privacy requirements that certain domains demand for their data, one solution is to move towards distributed, collaborative and multi-actor machine learning. This implies the development of a notion of contributivity, to quantify the participation of a partner to the final model. The definition of such a notion is far from being immediate. Being able to easily implement, experiment and compare different approaches is therefore mandatory. It requires a simulation tool, which we have undertaken to create, in the form of an open source Python library. Its development is ongoing, in the context of a workgroup bringing together several partners.

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The MELLODDY consortium brings together 17 partner organizations of different types, working towards a common goal, in multiple countries with different cultures. The project is fully remote, with various businesses and technicals skills brought in by the partners. How does one develop transversality to a project in this context? How to create a common way of working in such an innovative and new collaboration endeavor? That is what Substra Foundation is trying to contribute to...

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“More sharing gets more data, more data creates more values but also worries”. This blog post is the first guest post on the Substra Foundation blog. It is written by Noggin, a digital, data & analytics company based in Singapore.

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