May 2023
Intermediate to advanced
336 pages
10h 3m
English
This chapter covers
In previous chapters we’ve looked into differential privacy, local differential privacy, privacy-preserving synthetic data generation, privacy-preserving data mining, and their application when designing privacy-preserving machine learning solutions. As you’ll recall, in differential privacy a trusted data curator collects data from individuals and produces differentially private results by adding precisely computed noise to the aggregation of individuals’ ...