TR22-113 Authors: Yanyi Liu, Rafael Pass

Publication: 14th August 2022 11:05

Downloads: 162

Keywords:

A central open problem in complexity theory concerns the question of

whether all efficient randomized algorithms can be simulated by

efficient deterministic algorithms. The celebrated ``hardness

v.s. randomness” paradigm pioneered by Blum-Micali (SIAM JoC’84),

Yao (FOCS’84) and Nisan-Wigderson (JCSS’94) presents hardness

assumptions under which $\prBPP = \prP$, but these hardness assumptions

are not known to also be necessary for such derandomization.

In this work, following the recent work by Chen and Tell (FOCS’21) that

considers “almost-all-input” hardness of a function $f$ (i.e., hardness

of computing $f$ on more than a finite number of inputs), we consider

“almost-all-input” \emph{leakage-resilient hardness} of a function $f$

—that is, hardness of computing $f(x)$ even given, say, $\sqrt{|x|}$

bits of leakage of $f(x)$. We show that leakage-resilient hardness

characterizes derandomization of $\prBPP$ (i.e., gives a both \emph{

necessary} and \emph{sufficient} condition for derandomization). In more

detail, we show that there exists a constant $c$ such that the following

are equivalent:

- $\prBPP = \prP$;

- Existence of a poly-time computable function $f :\{0, 1\}^n \rightarrow \{0,1\}^n$

that is almost-all-input leakage-resilient hard with respect to

$n^c$-time probabilistic algorithms.

Our characterization naturally extends also to the low-end

derandomization regime, to derandomization of $\prMA$, and also to

average-case derandomization, by appropriately weakening the

requirements on the function $f$.