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Reports tagged with pseudorandom generator:
TR00-023 | 11th May 2000
Michael Alekhnovich, Eli Ben-Sasson, Alexander Razborov, Avi Wigderson

Pseudorandom Generators in Propositional Proof Complexity

We call a pseudorandom generator $G_n:\{0,1\}^n\to \{0,1\}^m$ {\em
hard} for a propositional proof system $P$ if $P$ can not efficiently
prove the (properly encoded) statement $G_n(x_1,\ldots,x_n)\neq b$ for
{\em any} string $b\in\{0,1\}^m$. We consider a variety of
``combinatorial'' pseudorandom generators inspired by the
Nisan-Wigderson generator on the one hand, and ... more >>>

TR01-007 | 7th December 2000
Vered Rosen

On the Security of Modular Exponentiation

Comments: 1

Assuming the inractability of factoring, we show that the
output of the exponentiation modulo a composite function
$f_{N,g}(x)=g^x\bmod N$ (where $N=P\cdot Q$) is pseudorandom,
even when its input is restricted to be half the size.
This result is equivalent to the simultaneous hardness of
the ... more >>>

TR04-020 | 8th March 2004
Emanuele Viola

The Complexity of Constructing Pseudorandom Generators from Hard Functions

We study the complexity of building
pseudorandom generators (PRGs) from hard functions.

We show that, starting from a function f : {0,1}^l -> {0,1} that
is mildly hard on average, i.e. every circuit of size 2^Omega(l)
fails to compute f on at least a 1/poly(l)
fraction of inputs, we can ... more >>>

TR04-074 | 26th August 2004
Emanuele Viola

On Parallel Pseudorandom Generators

Revisions: 1

We study pseudorandom generator (PRG) constructions $G^f : {0,1}^l \to {0,1}^{l+s}$ from one-way functions $f : {0,1}^n \to {0,1}^m$. We consider PRG constructions of the form $G^f(x) = C(f(q_{1}) \ldots f(q_{poly(n)}))$
where $C$ is a polynomial-size constant depth circuit
and $C$ and the $q$'s are generated from $x$ arbitrarily.
more >>>

TR05-043 | 5th April 2005
Emanuele Viola

Pseudorandom Bits for Constant-Depth Circuits with Few Arbitrary Symmetric Gates

We exhibit an explicitly computable `pseudorandom' generator stretching $l$ bits into $m(l) = l^{\Omega(\log l)}$ bits that look random to constant-depth circuits of size $m(l)$ with $\log m(l)$ arbitrary symmetric gates (e.g. PARITY, MAJORITY). This improves on a generator by Luby, Velickovic and Wigderson (ISTCS '93) that achieves the same ... more >>>

TR07-081 | 10th August 2007
Andrej Bogdanov, Emanuele Viola

Pseudorandom bits for polynomials

We present a new approach to constructing pseudorandom generators that fool low-degree polynomials over finite fields, based on the Gowers norm. Using this approach, we obtain the following main constructions of explicitly computable generators $G : \F^s \to \F^n$ that fool polynomials over a prime field $\F$:
\item a ... more >>>

TR07-132 | 8th December 2007
Emanuele Viola

The sum of d small-bias generators fools polynomials of degree d

We prove that the sum of $d$ small-bias generators $L
: \F^s \to \F^n$ fools degree-$d$ polynomials in $n$
variables over a prime field $\F$, for any fixed
degree $d$ and field $\F$, including $\F = \F_2 =

Our result improves on both the work by Bogdanov and
Viola ... more >>>

TR09-016 | 21st February 2009
Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco Servedio, Emanuele Viola

Bounded Independence Fools Halfspaces

We show that any distribution on {-1,1}^n that is k-wise independent fools any halfspace h with error \eps for k = O(\log^2(1/\eps)/\eps^2). Up to logarithmic factors, our result matches a lower bound by Benjamini, Gurel-Gurevich, and Peled (2007) showing that k = \Omega(1/(\eps^2 \cdot \log(1/\eps))). Using standard constructions of k-wise ... more >>>

TR10-077 | 26th April 2010
Venkatesan Guruswami, Adam Smith

Codes for Computationally Simple Channels: Explicit Constructions with Optimal Rate

In this paper, we consider coding schemes for computationally bounded channels, which can introduce an arbitrary set of errors as long as (a) the fraction of errors is bounded with high probability by a parameter p and (b) the process which adds the errors can be described by a sufficiently ... more >>>

TR10-175 | 14th November 2010
Emanuele Viola

Randomness buys depth for approximate counting

Revisions: 1

We show that the promise problem of distinguishing $n$-bit strings of hamming weight $\ge 1/2 + \Omega(1/\log^{d-1} n)$ from strings of weight $\le 1/2 - \Omega(1/\log^{d-1} n)$ can be solved by explicit, randomized (unbounded-fan-in) poly(n)-size depth-$d$ circuits with error $\le 1/3$, but cannot be solved by deterministic poly(n)-size depth-$(d+1)$ circuits, ... more >>>

TR10-186 | 2nd December 2010
Bill Fefferman, Ronen Shaltiel, Chris Umans, Emanuele Viola

On beating the hybrid argument

The {\em hybrid argument}
allows one to relate
the {\em distinguishability} of a distribution (from
uniform) to the {\em
predictability} of individual bits given a prefix. The
argument incurs a loss of a factor $k$ equal to the
bit-length of the
distributions: $\epsilon$-distinguishability implies only
$\epsilon/k$-predictability. ... more >>>

TR12-005 | 13th January 2012
Periklis Papakonstantinou, Guang Yang

A remark on one-wayness versus pseudorandomness

Every pseudorandom generator is in particular a one-way function. If we only consider part of the output of the
pseudorandom generator is this still one-way? Here is a general setting formalizing this question. Suppose
$G:\{0,1\}^n\rightarrow \{0,1\}^{\ell(n)}$ is a pseudorandom generator with stretch $\ell(n)> n$. Let $M_R\in\{0,1\}^{m(n)\times \ell(n)}$ be a linear ... more >>>

TR13-064 | 22nd April 2013
Anat Ganor, Ran Raz

Space Pseudorandom Generators by Communication Complexity Lower Bounds

Revisions: 1

In 1989, Babai, Nisan and Szegedy [BNS92] gave a construction of a pseudorandom generator for logspace, based on lower bounds for multiparty communication complexity. The seed length of their pseudorandom generator was $2^{\Theta(\sqrt n)}\,\,\,$, because the best lower bounds for multiparty communication complexity are relatively weak. Subsequently, pseudorandom generators for ... more >>>

TR17-025 | 16th February 2017
Pooya Hatami, Avishay Tal

Pseudorandom Generators for Low-Sensitivity Functions

A Boolean function is said to have maximal sensitivity $s$ if $s$ is the largest number of Hamming neighbors of a point which differ from it in function value. We construct a pseudorandom generator with seed-length $2^{O(\sqrt{s})} \cdot \log(n)$ that fools Boolean functions on $n$ variables with maximal sensitivity at ... more >>>

TR17-122 | 28th July 2017
Rohit Gurjar, Ben Lee Volk

Pseudorandom Bits for Oblivious Branching Programs

We construct a pseudorandom generator which fools read-$k$ oblivious branching programs and, more generally, any linear length oblivious branching program, assuming that the sequence according to which the bits are read is known in advance. For polynomial width branching programs, the seed lengths in our constructions are $\tilde{O}(n^{1-1/2^{k-1}})$ (for the ... more >>>

TR18-100 | 18th May 2018
Eshan Chattopadhyay, Anindya De, Rocco Servedio

Simple and efficient pseudorandom generators from Gaussian processes

Revisions: 1

We show that a very simple pseudorandom generator fools intersections of $k$ linear threshold functions (LTFs) and arbitrary functions of $k$ LTFs over $n$-dimensional Gaussian space.

The two analyses of our PRG (for intersections versus arbitrary functions of LTFs) are quite different from each other and from previous analyses of ... more >>>

TR18-110 | 4th June 2018
Fu Li, David Zuckerman

Improved Extractors for Recognizable and Algebraic Sources

Revisions: 1

We study the task of seedless randomness extraction from recognizable sources, which are uniform distributions over sets of the form {x : f(x) = v} for functions f in some specified class C. We give two simple methods for constructing seedless extractors for C-recognizable sources.
Our first method shows that ... more >>>

TR18-155 | 8th September 2018
Eshan Chattopadhyay, Pooya Hatami, Shachar Lovett, Avishay Tal

Pseudorandom generators from the second Fourier level and applications to AC0 with parity gates

A recent work of Chattopadhyay et al. (CCC 2018) introduced a new framework for the design of pseudorandom generators for Boolean functions. It works under the assumption that the Fourier tails of the Boolean functions are uniformly bounded for all levels by an exponential function. In this work, we design ... more >>>

TR20-138 | 9th September 2020
William Hoza, Edward Pyne, Salil Vadhan

Pseudorandom Generators for Unbounded-Width Permutation Branching Programs

Revisions: 1

We prove that the Impagliazzo-Nisan-Wigderson (STOC 1994) pseudorandom generator (PRG) fools ordered (read-once) permutation branching programs of unbounded width with a seed length of $\widetilde{O}(\log d + \log n \cdot \log(1/\varepsilon))$, assuming the program has only one accepting vertex in the final layer. Here, $n$ is the length of the ... more >>>

TR21-002 | 8th January 2021
Pooya Hatami, William Hoza, Avishay Tal, Roei Tell

Fooling Constant-Depth Threshold Circuits

We present new constructions of pseudorandom generators (PRGs) for two of the most widely-studied non-uniform circuit classes in complexity theory. Our main result is a construction of the first non-trivial PRG for linear threshold (LTF) circuits of arbitrary constant depth and super-linear size. This PRG fools circuits with depth $d\in\mathbb{N}$ ... more >>>

TR21-019 | 17th February 2021
Edward Pyne, Salil Vadhan

Pseudodistributions That Beat All Pseudorandom Generators

A recent paper of Braverman, Cohen, and Garg (STOC 2018) introduced the concept of a pseudorandom pseudodistribution generator (PRPG), which amounts to a pseudorandom generator (PRG) whose outputs are accompanied with real coefficients that scale the acceptance probabilities of any potential distinguisher. They gave an explicit construction of PRPGs for ... more >>>

TR21-048 | 27th March 2021
William Hoza

Better Pseudodistributions and Derandomization for Space-Bounded Computation

Three decades ago, Nisan constructed an explicit pseudorandom generator (PRG) that fools width-$n$ length-$n$ read-once branching programs (ROBPs) with error $\varepsilon$ and seed length $O(\log^2 n + \log n \cdot \log(1/\varepsilon))$ (Combinatorica 1992). Nisan's generator remains the best explicit PRG known for this important model of computation. However, a recent ... more >>>

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