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Lecture
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This lecture covers techniques for data privacy, including differential privacy and k-anonymity. It discusses concepts such as randomization guarantees, data perturbation, quasi-identifiers, attacks on k-anonymity, and differential privacy strength. The instructor explains how differential privacy ensures statistical insignificance for neighboring databases and the Laplace Mechanism as a tool to achieve it.
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