Chernoff-like Concentration Bounds on Permutations

Suppose I have $n$ balls. Among them, there are $m \leq n$ black balls and the other $n - m$ balls are white. Fix a random permutation $\pi$ over these balls and denote by $Y_i$ the number of black balls in the first $i$ positions of our permutation $\pi$. I would like to show that $Y_i$ is sharply concentrated around its mean.

The expected value

It is not hard to compute the expected value of $Y_i$: the probability that a black ball appears somewhere along the first $i$ positions is $i/n$; moreover, there are $m$ black balls, hence $$\mathbb{E}[Y_i] = \frac{m\cdot i}{n}.$$

But can we show that $Y_i$ is actually very close to its mean?

A failed attempt

For every black ball $b$, one can define an indicator variable $X_b$ which equals 1 if $b$ appears in the first $i$ positions of $\pi$ and 0 otherwise. Denoting the set of all black balls by $B$, we have

$$Y_i = \sum_{b \in B} X_b.$$

I was hoping to be able to use a Chernoff bound type of a concentration bound, but unfortunately for two black balls $b$ and $b'$, $X_b$ and $X_{b'}$ are not completely independent. If $X_b = 1$, then there are only $i-1$ spots left for $b'$ to appear in the first $i$ locations and hence the probability of $X_{b'}$ being 1 gets smaller.

$$\Pr \Big[\big|Y_i - \mathbb{E}[Y_i]\big| \geq t \Big] \leq \exp\Big(\frac{-(n-1)t^2}{2(n-i)(i-1)} \Big)$$