Why the definition in $\epsilon$-differential privacy is multiplicative rather than additive？ According to its mathematical definition, a random algorithm $M: D\rightarrow R$ satisfies $\epsilon$-differential privacy if the adjacent datasets $x, y \in D$ where $D$ is a whole dataset and datasets $x$ and $y$ differs by only one record, and any set of $S \in Range(M)$,
$Pr(M(x) \in S) \leq Pr(M(y) \in S) * e^{\epsilon}$.

The additive one is shown in this question: Differential privacy definition.

Dr. Dwork explains the advantage of using the multiplicative definition over the additive one in the Microsoft Research Lecture 2 (at about 2'50''). In short, with this multiplicative definition, it could be ruled out the possibility that an individual's record would be randomly selected and published.
However, I struggle to understand her meaning. I would appreciate any help in understanding this definition!
In short, with this multiplicative definition, it could be ruled out the possibility that an individual's record would be randomly selected and published.


Consider a malicious algorithm $M^*$ that picks a random individual's record from the input database (of size $n$) and outputs it. Note that this $M^*$ should not be considered secure for a good differential privacy definition because it compromises the privacy of a random individual. 

However, the additive differential privacy definition regards $M^*$ as a secure algorithm for $\epsilon=1/n$. To see this, consider two input databases $X,Y$ (both of size $n$) differ with a single element $d$ (where $d\in X$ but $d\not\in Y$). Then, it's not hard to see $\Pr[M^*(X)\in S]\leq\Pr[M^*(Y)\in S]+1/n$ holds for any subset $S\subseteq R$, i.e., $M^*$ satisfies the additive $1/n$-differential privacy. 

The above issue is fixed by the standard multiplicative definition. Note that for $S=\{d\}$, there is no $\epsilon$ such that $\Pr[M^*(X)\in S]\leq\Pr[M^*(Y)\in S]\cdot e^\epsilon$ because $\Pr[M^*(X)=d]=1/n&gt;0$ and $\Pr[M^*(Y)=d]=0$. That is, $M^*$ does not satisfy the standard $\epsilon$-differential privacy.