Differential privacy typically involves adding a controlled amount of random noise to the data or the outputs of data analyses. This noise ensures that the results are statistically similar regardless of whether any single individualâs data is included or not. The level of noise introduced is determined by a parameter called epsilon (ε), which balances the trade-off between privacy and data utility.
