Lecture

Data Privacy: Techniques and Challenges

In course

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Description

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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Instructor

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Official source

https://mediaspace.epfl.ch/media/0_kvgi1wfn

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Ontological neighbourhood

Computer engineering

Computer security: Privacy

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