Differential Privacy Framework:
At its core, differential privacy focuses on minimizing the impact that
the inclusion or exclusion of a single data point can have on the
outcome of a query or analysis. It introduces a privacy parameter (often
denoted as ε) that quantifies the maximum allowable change in the
analysis result when an individual’s data is added or
removed.2 A smaller ε value indicates a stronger
privacy guarantee, as it limits the amount of information that an
adversary can glean about a specific individual from the analysis
outcome.