Differential privacy algorithms
Differential privacy is a fundamental concept that’s designed to safeguard individual privacy while enabling the statistical analysis of sensitive data. It establishes a mathematical framework that guarantees the preservation of individual privacy during data analysis and sharing processes. Differential privacy algorithms play a vital role by introducing random noise into the data, making it challenging to identify specific records.
In the preceding chapter, we learned that the core idea behind differential privacy is to introduce random noise into the data analysis process. This noise makes it difficult for an attacker to determine whether a particular individual’s data was included in the analysis, thus preserving privacy. The fundamental concept is that the inclusion or exclusion of any individual’s data should not significantly impact the results of the analysis:
Figure 4.1 – Illustrating...
