Making data open and available to all helps us all understand our world and are thus better informed to shape the policies to run it. But such openness does come with problems - one in particular is the invasion of people's privacy. Detailed census information about household income helps debate and planning for local government, but can reveal personal information that citizens reasonably prefer to keep private. Privacy Enhancing Technologies are tools that can finesse this problem. My colleague Katharine Jarmul is a data scientist who is also an activist for personal privacy. Here she introduces three of these tools that are usable now: Differential Privacy, Distributed & Federated Analysis & Learning, and Encrypted Computation.
Inicia sesión para agregar comentarios
Otros mensajes en este grupo.
How is code generation with GenAI different from more "traditional" code generators? The newest memo in Birgitta Böckeler's explorations of GenAI talks abou
How is code generation with GenAI different from more "traditional" code generators? The newest memo in Birgitta Böckeler's explorations of GenAI talks abou
Many s
At Thoughtworks, we are strong practitioners of Test Driven Development (TDD). Naturally this leads to the question of how generative AI can
