Lecture

Data Privacy: Techniques and Challenges

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.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.