Differential privacy seeks
to protect individual data values by adding statistical
noise to the analysis process. The math involved in adding
the noise is complex, but the principle is fairly intuitive. The noise ensures that data aggregation
stay statistically consistent with the actual data values, allowing for some
random variation, but make it possible to work out the individual values
from the aggregated data. In addition, the noise is different for
each analysis, so the results are non deterministic. In other words, to analysis that perform
the same aggregation may produce slightly different results.