Consider the two vector distributions $\xi,\chi$ described below, each one outputting integer vectors of length $n$ with coefficients in $\{0,\dots,n\}$.
Distribution $\xi$ samples each coefficient $v_i$ following a distribution $\alpha_i\sim Binomial(n,p)$ and outputs $\mathbf{v}=(v_1,\dots,v_{n})$. The $\alpha_i$'s are pairwise independent.
Distribution $\chi$ samples each coefficient $w_i$ following a distribution $\beta_i\sim Binomial(n,p)$ and outputs $\mathbf{w}=(w_1,\dots,w_{n})$. The $\beta_j$'s are dependent: for each $a\neq b$, we have $$\operatorname{Corr}(\beta_a,\beta_b)=r>0,$$
where $\operatorname{Corr}=\operatorname{Cov}(\beta_a,\beta_b)/\sigma_a\sigma_b$ is the Pearson correlation coefficient.
I know both distributions are distinguishable, but how many samples do we need to actually distinguish them?
Let's say that the use of distribution $\xi$ is secure in a protocol. Is the use of $\chi$ less secure ? I.e., does correlation helps the attacker? The answer depends maybe on the attack, but is there a general result or fact that will help me understand to what extent is correlation dangerous?