I'm reading Section 2.1 of Chapter 2 in Villani's textbook Topics in Optimal Transportation.
Let $X := \mathbb R^d$, $\mu$ a Borel probability measure on $X$, and $\varphi : X \to \mathbb R \cup \{+\infty\}$ convex. A subset $A$ of $X$ is called a small set if and only if the Hausdorff dimension of $A$ is at most $d-1$. Let $D := \{x \in X \mid \varphi (x) < +\infty\}$ be the domain of $f$. Then $D$ is convex.
Theorem: Let $E := \{x \in \operatorname{int} (D) \mid \varphi \text{ is not Fréchet differentiable at }x\}$. If $\varphi \in L_1 (\mu)$, then $E$ is measurable and a small set.
It follows from $\varphi \in L_1 (\mu)$ that $D$ is measurable and $\mu (D) =1$. Let $K := \operatorname{int} (D)$ and $$ F := \{x \in K \mid \varphi \text{ is Fréchet differentiable at }x\}. $$
Clearly, $E$ is measurable if and only if $F$ is measurable. In case $d=1$, we can prove that $F$ is measurable and $E$ is countable. This directly implies the theorem.
Could you elaborate on or provide a reference of how to prove the theorem in case $d > 1$?