Introducing differential privacy

Differential privacy is a concept that has the purpose of protecting the privacy of individual data contributors while still allowing useful statistical analysis. The basic idea behind differential privacy is to add noise or random perturbations to the data in such a way that the statistical properties of the dataset stay the same, but it is much more difficult to identify individual information within the dataset.

The level of privacy protection in differential privacy is controlled by a parameter called epsilon (ε). A smaller value of epsilon indicates a higher level of privacy, but it might also lead to a decrease in data utility (usefulness of the data for analysis). Striking a balance between privacy and utility is a key challenge in implementing differential privacy:

Figure 5.3 – Epsilon (Ɛ) value relationship with privacy and accuracy

A library that we can use to add noise to the data is the...