Understanding $\varepsilon$-advantage in cryptographically secure deterministic RNG In Efficient and Secure Pseudo-Random Number Generation by Vazirani &amp; Vazirani, it is stated that every pseudorandom number generator which satisfies the XOR Condition can securely output $\log n$ bits (where $\ n = |N|$). Such a condition is satisfied by the Blum-Blum-Shub (whose trapdoor is based on the quadratic residuosity assumption), thus up to $\log \log N $ bits at each step can be extracted.

My question is of information theoretic interest: what exactly do they mean when they state that the boolean predicates (the output bits) $b_1,\ldots,b_k$ are inversion secure if there exists a Las Vegas algorithm T that runs in probabilistic polytime, i.e $T^{O_{b_{i},N}}[i, E_{N}(x)] = x$ (with $E_{N}(x)$ the one-way function), where $O_{b_{i},N}$ is a $\frac{1}{2} + \frac{1}{poly(n)}$ advantage oracle for $b_i$ with respect to N?
Why the probability beyond 1/2 plus that $\varepsilon$-advantage, which, I assume, bounds the overall probability? So, if the XOR of each non-empty subset of these predicates is inversion secure,
boolean predicates satisfy the XOR Condition.

I think it's pretty close with what Rabin function says:


  If for a $\frac{1}{logN}$ fraction of the quadratic residues $q\pmod N$ one could find a square root of $q$, then one could factor $N$ in random polynomial time.


Still, where does $1/(\log N)$ come from?
Took a while to grasp it, but with the help of a friend we came to a conclusion (and it turns out it was rather easy).

Recall that a function $f_n : \{0,1\}^{n} \mapsto \{0,1\}^{m(n)}$ is said to be one-way if for a positive polynomial $poly(\cdot)$, every randomized algorithm running in probabilistic polynomial time inverts $f_n$ with a negligible probability, i.e.  $\Pr[A\:inverting\: f_n] &lt; \frac{1}{poly(n)}$, with $n$ size of the input. This polynomial comes from the fact that for every possible computational path, the algorithm will follow it with a probability $2^{-n}$, which is bounded by $\mathcal O(\frac{1}{n^c})$ for every constant $c$. Clearly, we want the adversary's advantage to be as asymptotically small as possible.