# what is the relationship between epsilon and sensitivity in the Differential-Privacy?

In some Differential-Privacy(DP) papers, they use epsilon as the x-axis in the figures of the experiments' result while other papers use the sensitivity.

1. What is the relationship between epsilon and sensitivity in the DP?

2. How can I compare the results of different papers?

Differential privacy is usually obtained by 1. computing a function $$F$$ of the data and 2. adding some noise to the result. The noise must be large enough to hide an individual contribution, and "how well an individual contribution is hidden" is captured by the parameter $$\varepsilon$$.
But for some functions $$F$$, an individual contribution can change the true result more than for others functions. For example:
That concept is captured by sensitivity: the higher the possible change, the higher the sensitivity. And typically, to get $$\varepsilon$$-differential privacy with a fixed $$\varepsilon$$, you have to add more noise when the sensitivity is larger (to compensate). That's the typical relationship between the two concepts.