Revision #2 Authors: Yanyi Liu, Rafael Pass

Accepted on: 16th May 2023 00:40

Downloads: 232

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 e.g., $\prBPP = \prP$ (so-called ``high-end

derandomization), or $\prBPP \subseteq \prSUBEXP$ (so-called

``low-end derandomization), and more generally, under which

$\prBPP \subseteq \prDTIME(\C)$ where $\C$ is a ``nice'' class

(closed under composition with a polynomial), 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),

both in the high-end and in the low-end setting.

In more detail, we show that there exists a constant $c$ such that for

every function $T$, the following are equivalent:

- $\prBPP \subseteq \prDTIME(\poly(T(\poly(n))))$;

- Existence of a $\poly(T(\poly(n)))$-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.

As far as we know, this is the first assumption that

characterizes derandomization in both the low-end and the high-end regime.

Additionally, our characterization naturally extends also

to derandomization of $\prMA$, and also to

average-case derandomization, by appropriately weakening the

requirements on the function $f$. In particular, for the case of

average-case (a.k.a. ``effective'') derandomization, we longer require

the function to be almost-all-input hard, but simply satisfy the more

standard notion of average-case leakage-resilient hardness (w.r.t.,

every samplable distribution), whereas for derandomization of $\prMA$,

we instead consider leakage-resilience for relations.

Revision #1 Authors: Yanyi Liu, Rafael Pass

Accepted on: 10th February 2023 22:54

Downloads: 138

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 e.g., $\prBPP = \prP$ (so-called ``high-end derandomization), or $\prBPP \subseteq

\prSUBEXP$ (so-called ``low-end derandomization), and more generally, under which $\prBPP \subseteq

\prDTIME(\C)$ where $\C$ is a ``nice'' class (closed under

composition with a polynomial), 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),

both in the high-end and in the low-end setting.

In more detail, we show that there exists a constant $c$ such that for

every function $T$, the following are equivalent:

- $\prBPP \subseteq \prDTIME(\poly(T(\poly(n))))$;

- Existence of a $\poly(T(\poly(n)))$-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.

As far as we know, this is the first assumption that

characterizes derandomization in both the low-end and the high-end regime.

Additionally, our characterization naturally extends also

to derandomization of $\prMA$, and also to

average-case derandomization, by appropriately weakening the

requirements on the function $f$. In particular, for the case of

average-case (a.k.a. ``effective'') derandomization, we longer require

the function to be almost-all-input hard, but simply satisfy the more

standard notion of average-case leakage-resilient hardness (w.r.t.,

every samplable distribution), whereas for derandomization of $\prMA$,

we instead consider leakage-resilience for relations.

TR22-113 Authors: Yanyi Liu, Rafael Pass

Publication: 14th August 2022 11:05

Downloads: 599

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 $f$.