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This question follows on from the question Circular hash collision where digest is pre-image: Could hash(a) = b, hash(b)=c, then hash(c)=a?

The question was answered but the issue of the number of collisions was also raised. I have moved my answer to this query as a separate question. The other question begins:

For any SHA-family of algorithms (including Keccak) except for SHA1, would it be theoretically possible for there to exist a pre-image a whose hash digest b is the pre-image to the hash digest c which is the pre-image to a thus completing a circle (i.e. circular hash or collision of pre-image a with the digest a)?

${{hash(A)\to B}}$, ${{hash(B)\to C}}$ , ${{hash(C)\to A}}$

The answer to this is yes. My question is, how many such collisions would typically exist?

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The expected number of 3-cycles in a random function on a large domain is $1/3$. More generally, for a random function on a set of size $n$, the expected number of cycles of length $\ell$ is asymptotically equivalent to $1/\ell$ as $n \to \infty$.

This result, along with a general approach to the combinatorical properties of random functions, can be found in the paper Random Mapping Statistics by Flajolet and Odlyzko (likely, the result has been derived elsewhere using different methods too).

Let me sketch how this result is obtained. Recall that the functional graph of a mapping is a set of components, each of which consists of a cycle to which trees are connected.

Let $t(z)$ be the exponential generating function for the number of (rooted) trees. It can be shown that (this is known as Cayley's formula)

$$t(z) = \sum_{n = 1}^\infty \frac{n^{n - 1}}{n!} z^n.$$

Since components are cycles of trees, the generating function for the number of components is

$$\log\left(\frac{1}{1-t(z)}\right).$$

Also, the generating function for the number of functional graphs would then be the exponential of the above. In order to count the number of cycles of length $\ell$, one can construct a bivariate generating function for the number of functional graphs in which the second variable "marks" $\ell$-cycles. That is, instead of the above generating function for the number of components, we define

$$\log\left(\frac{1}{1-t(z)}\right) + (u- 1)\frac{t(z)^\ell}{\ell}.$$

That is, the term (in the Taylor series in the variable $t(z)$) corresponding to cycles of length $\ell$ is multiplied by the marker variable $u$. The desired bivariate generating function at the level of the functional graph is then

$$\xi(z, u) = \exp\left(\log\left(\frac{1}{1-t(z)}\right) + (u- 1)\frac{t(z)^\ell}{\ell}\right).$$

One can see that the generating function for the sum of number of cycles of length $\ell$ over all functional graphs of fixed size can then be computed as

$$\Xi(z) = \frac{\partial\xi(z, u)}{\partial u}\Bigg|_{u = 1} = \frac{z^\ell}{\ell(1 - t(z))}.$$

It remains to determine the Maclaurin series coefficients of the above, at least for terms of very high order. This is possible by singularity analysis. We use the fact (by Proposition 1 in the paper of Flajolet and Odlyzko) that (as $z \to 1/e$)

$$1 - t(z) \sim \sqrt{2(1 - ez)}.$$

Hence,

$$\Xi(z) \sim \frac{z^\ell}{\ell\sqrt{2(1 - ez)}}.$$

The singularity analysis technique (Theorem 1 in Flajolet and Odlyzko) then shows that (with $\Xi_n / n!$ the coefficient of order $n$)

$$\Xi_n \overset{\star_1}{\sim} e^n\,\frac{n!}{\sqrt{2}\ell}\,\frac{\sqrt{n}}{n\Gamma(1/2)} \sim \frac{e^n n!}{\ell\sqrt{2\pi n}} \overset{\star_2}{\sim} \frac{n^n}{\ell},$$

where $\star_1$ is the actual singularity analysis and $\star_2$ follows from Stirling's approximation $n! \sim \sqrt{2\pi n}\, n^n/e^n$. In order to obtain the expected value of the number of $\ell$-cycles of a random function on a set of size $n$, it suffices to divide by the number of such functions ($n^n$): $\Xi_n \sim 1/\ell$.

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I want argue heuristically that many such $3-$cycles are unlikely. Consider a random function $f$ mapping $X:=\{1,\ldots,n\}$ to itself. @MeirMaor showed that at least one such cycle has significant probability.

Consider two iterations of $f$ and note that the image set $Y=f(f(X)):=h(x)$ will typically have size roughly $$(1-e^{-1})^2\cdot n:=c^{-1} \cdot n.$$ Thus, on average, the inverse image $S_y:=h^{-1}(y)$ of each $y \in Y$ has size $c\approx 2.5.$ The argument I make would need floor and ceiling functions but the essence stays the same.

Edit: The problem is harder than I thought. Thus, one needs to be a bit more careful, since in general non-uniform distributions amplify the collision probabilities, and the distribution of balls in roughly $c^{-1}\cdot n$ bins after the second iteration won't in general be flat.

If there was a single iteration of $h$ the maximal load would be with high probability $\log n/\log \log n$. After the second iteration, the maximal load is at most $(\log n/\log \log n)^2$, with high probability. This upper bound could be tightened by a tiny factor, but this is insignificant. See https://en.wikipedia.org/wiki/Balls_into_bins_problem, and the reference to the paper by Raab.

Fix $y\in Y$ and note that if $f$ maps a point $y\in Y$ to a point in $S_y$ a $3-$cycle containing $y$ is completed. This has relatively low probability $c/n$ of happening and if it doesn't occur it means that $y$ is mapped to some other $S_{y'},$ thus reducing the probability that $f(y')$ completes a cycle containing $y'$ to $(c-1)/(n-1).$ This is because the inverse images $$\{S_y: y \in Y\}$$ form a partition of $X,$ and a point not in $S_y$ is in some other $S_{y'}.$

We now first approximate what happens by first ignoring the fact that $S_y$ are of varying size, approximate the number of $3-$cycles by a Binomial distribution $$\mathrm{Bin}(n/c,c/n)$$ which has expectation $1$ and a rapidly decaying tail. This distribution only represents where $h(h(h(x)))$ falls in $X.$

However, what we really need is to weigh this distribution linearly by the actual size of the set $S_y$ in case it is hit. To obtain our upper bound we use the maximal size $(\log n/\log \log n)^2,$ giving an upper bound $O((\log n/\log \log n)^2)$ since the binomial well approximated by a poisson distribution with rate $1,$ and thus the probability still decays exponentially fast.

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