ECCC-Report TR22-145https://eccc.weizmann.ac.il/report/2022/145Comments and Revisions published for TR22-145en-usFri, 01 Mar 2024 02:36:59 +0200
Revision 1
| Revisiting Time-Space Tradeoffs for Function Inversion |
Alexander Golovnev,
Siyao Guo,
Spencer Peters,
Noah Stephens-Davidowitz
https://eccc.weizmann.ac.il/report/2022/145#revision1We study the black-box function inversion problem, which is the problem of finding $x \in [N]$ such that $f(x) = y$, given as input some challenge point $y$ in the image of a function $f : [N] \to [N]$, using $T$ oracle queries to $f$ and preprocessed advice $\sigma \in \{0,1\}^S$ depending on~$f$. We prove a number of new results about this problem, as follows.
1. We show an algorithm that works for any $T$ and $S$ satisfying $T S^2 \cdot \max\{S,T\} = \widetilde{\Theta}(N^3)$.
In the important setting when $S < T$, this improves on the celebrated algorithm of Fiat and Naor [STOC, 1991], which requires $T S^3 \gtrsim N^3$. E.g., Fiat and Naor's algorithm is only non-trivial for $S \gg N^{2/3}$, while our algorithm gives a non-trivial tradeoff for any $S \gg N^{1/2}$. (Our algorithm and analysis are quite simple. As a consequence of this, we also give a self-contained and simple proof of Fiat and Naor's original result, with certain optimizations left out for simplicity.)
2. We observe that there is a very simple non-adaptive algorithm (i.e., an algorithm whose $i$th query $x_i$ is chosen based entirely on $\sigma$ and $y$, and not on the $f(x_1),\ldots, f(x_{i-1})$) that improves slightly on the trivial algorithm. It works for any
$T$ and $S$ satisfying $S = \Theta(N \log(N/T))$, for example, $T = N /\polylog(N)$, $S = \Theta(N/\log \log N)$.
This answers a question due to Corrigan-Gibbs and Kogan [TCC, 2019], who asked whether non-trivial non-adaptive algorithms exist; namely, algorithms that work with parameters $T$ and $S$ satisfying $T + S/\log N < o(N)$.
We also observe that our non-adaptive algorithm is what we call a guess-and-check algorithm, that is, it is non-adaptive and its final output is always one of the oracle queries $x_1,\ldots, x_T$.
For guess-and-check algorithms, we prove a matching lower bound,
therefore completely characterizing the achievable parameters $(S,T)$ for this natural class of algorithms.
(Corrigan-Gibbs and Kogan showed that any such lower bound for arbitrary non-adaptive algorithms would imply new circuit lower bounds.)
3. We show equivalence between function inversion and a natural decision version of the problem in both the worst case and the average case, and similarly for functions $f : [N] \to [M]$ with different ranges.
All of the above results are most naturally described in a model with shared randomness (i.e., random coins shared between the preprocessing algorithm and the online algorithm). However, as an additional contribution, we show (using a technique from communication complexity due to Newman [IPL, 1991]) how to generically convert any algorithm that uses shared randomness into one that does not.Fri, 01 Mar 2024 02:36:59 +0200https://eccc.weizmann.ac.il/report/2022/145#revision1
Paper TR22-145
| Revisiting Time-Space Tradeoffs for Function Inversion |
Alexander Golovnev,
Siyao Guo,
Spencer Peters,
Noah Stephens-Davidowitz
https://eccc.weizmann.ac.il/report/2022/145We study the black-box function inversion problem, which is the problem of finding $x \in [N]$ such that $f(x) = y$, given as input some challenge point $y$ in the image of a function $f : [N] \to [N]$, using $T$ oracle queries to $f$ and preprocessed advice $\sigma \in \{0,1\}^S$ depending on $f$. We prove a number of new results about this problem, as follows.
1. We show an algorithm that works for any $T$ and $S$ satisfying $T S^2 \cdot \max\{S,T\} = \widetilde{\Theta}(N^3)$. In the important setting when $S < T$, this improves on the celebrated algorithm of Fiat and Naor [STOC, 1991], which requires $T S^3 \geq N^3$. E.g., Fiat and Naor's algorithm is only non-trivial for $S \gg N^{2/3}$, while our algorithm gives a non-trivial tradeoff for any $S \gg N^{1/2}$. (Our algorithm and analysis are quite simple. As a consequence of this, we also give a self-contained and simple proof of Fiat and Naor's original result, with certain optimizations left out for simplicity.)
2. We show a non-adaptive algorithm (i.e., an algorithm whose $i$th query $x_i$ is chosen based entirely on $\sigma$ and $y$, and not on the $f(x_1),\ldots, f(x_{i-1})$) that works for any $T$ and $S$ satisfying $S = \Theta(N \log(N/T))$ giving the first non-trivial non-adaptive algorithm for this problem. E.g., setting $T = N/ \mathrm{poly\,log}(N)$ gives $S = \Theta(N \log \log N)$. This answers a question due to Corrigan-Gibbs and Kogan [TCC, 2019], who asked whether it was possible for a non-adaptive algorithm to work with parameters $T$ and $S$ satisfying $T + S/\log N < o(N)$. We also observe that our non-adaptive algorithm is what we call a guess-and-check algorithm, that is, it is non-adaptive and its final output is always one of the oracle queries $x_1,\ldots, x_T$. For guess-and-check algorithms, we prove a matching lower bound, therefore completely characterizing the achievable parameters $(S,T)$ for this natural class of algorithms. (Corrigan-Gibbs and Kogan showed that any such lower bound for arbitrary non-adaptive algorithms would imply new circuit lower bounds.)
3. We show equivalence between function inversion and a natural decision version of the problem in both the worst case and the average case, and similarly for functions $f : [N] \to [M]$ with different ranges.
All of the above results are most naturally described in a model with shared randomness (i.e., random coins shared between the preprocessing algorithm and the online algorithm). However, as an additional contribution, we show (using a technique from communication complexity due to Newman [IPL, 1991]) how to generically convert any algorithm that uses shared randomness into one that does not.Wed, 09 Nov 2022 19:55:48 +0200https://eccc.weizmann.ac.il/report/2022/145