Weizmann Logo
ECCC
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



REPORTS > KEYWORD > NC1:
Reports tagged with NC1:
TR11-095 | 22nd June 2011
Christoph Behle, Andreas Krebs, Klaus-Joern Lange, Pierre McKenzie

Low uniform versions of NC1

Revisions: 1

In the setting known as DLOGTIME-uniformity,
the fundamental complexity classes
$AC^0\subset ACC^0\subseteq TC^0\subseteq NC^1$ have several
robust characterizations.
In this paper we refine uniformity further and examine the impact
of these refinements on $NC^1$ and its subclasses.
When applied to the logarithmic circuit depth characterization of $NC^1$,
some refinements leave ... more >>>


TR18-069 | 14th April 2018
Oded Goldreich, Guy Rothblum

Constant-round interactive proof systems for AC0[2] and NC1

Revisions: 1

We present constant-round interactive proof systems for sufficiently uniform versions of AC0[2] and NC1.
Both proof systems are doubly-efficient, and offer a better trade-off between the round complexity and the total communication than
the work of Reingold, Rothblum, and Rothblum (STOC, 2016).
Our proof system for AC0[2] supports a more ... more >>>


TR18-149 | 25th August 2018
Craig Gentry, Charanjit Jutla

Obfuscation using Tensor Products

We describe obfuscation schemes for matrix-product branching programs that are purely algebraic and employ matrix algebra and tensor algebra over a finite field. In contrast to the obfuscation schemes of Garg et al (SICOM 2016) which were based on multilinear maps, these schemes do not use noisy encodings. We prove ... more >>>


TR24-040 | 29th February 2024
Kuan Cheng, Ruiyang Wu

Randomness Extractors in $\mathrm{AC}^0$ and $\mathrm{NC}^1$: Optimal up to Constant Factors

We study extractors computable in uniform $\mathrm{AC}^0$ and uniform $\mathrm{NC}^1$.

For the $\mathrm{AC}^0$ setting, we give a construction such that for every $k \ge n/ \mathrm{poly} \log n, \eps \ge 2^{-\mathrm{poly} \log n}$, it can extract $(1-\gamma)k$ randomness from an $(n, k)$ source for an arbitrary constant ... more >>>




ISSN 1433-8092 | Imprint