# What's the meaning of probabilities in differential privacy formula? I don't understand what does it mean by "The probability is taken is over the coin tosses of K."

Does it mean, the probability distribution is generated based on exactly same data but only the function is selected randomly?

So it's the data that ensures the differential privacy instead of the function, right?

For simplicity, assume there are $$N$$ randomized functions $$\mathcal{K}$$ possible, and one choose one uniformly with probability $$1/N.$$
For example, if we restricted ourselves to polynomials of degree $$\leq k$$ over $$GF(q),$$ there would be $$N=q^{k+1}$$ such possible functions and we choose a function by uniformly choosing each of its $$k+1$$ coordinates from $$GF(q)$$, thus with probability $$q^{-(k+1)}.$$
In this case the probability expression just means that $$\frac{\#\{\mathcal{K}: \mathcal{K}(D_1) \in S\}}{N}\leq \exp(\epsilon)\frac{\#\{\mathcal{K}: \mathcal{K}(D_2) \in S\}}{N},$$ and of course $$N$$ can be cancelled in the two sides of this equation. In general, the distribution may of course not be exactly uniform.