ልዩነት ግላዊነት

Differential privacy provides a formal definition of privacy: the output of an analysis should be almost the same whether or not any one individual is in the dataset. This is achieved by adding carefully calibrated random noise to results or computations, to bound differences in output distributions under the specified privacy parameters and neighboring-data definition. The strength is controlled by a parameter called epsilon (the 'privacy budget'): smaller epsilon means more noise and stronger privacy but lower accuracy. There are two main flavors. In the central model, a trusted curator holds raw data and adds noise to released answers. In the local model, each person's data is noised on their own device before it ever leaves, requiring no trusted central party but typically demanding more noise.