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.