64 research outputs found
Two Structural Results for Low Degree Polynomials and Applications
In this paper, two structural results concerning low degree polynomials over
finite fields are given. The first states that over any finite field
, for any polynomial on variables with degree , there exists a subspace of with dimension on which is constant. This result is shown to be tight.
Stated differently, a degree polynomial cannot compute an affine disperser
for dimension smaller than . Using a recursive
argument, we obtain our second structural result, showing that any degree
polynomial induces a partition of to affine subspaces of dimension
, such that is constant on each part.
We extend both structural results to more than one polynomial. We further
prove an analog of the first structural result to sparse polynomials (with no
restriction on the degree) and to functions that are close to low degree
polynomials. We also consider the algorithmic aspect of the two structural
results.
Our structural results have various applications, two of which are:
* Dvir [CC 2012] introduced the notion of extractors for varieties, and gave
explicit constructions of such extractors over large fields. We show that over
any finite field, any affine extractor is also an extractor for varieties with
related parameters. Our reduction also holds for dispersers, and we conclude
that Shaltiel's affine disperser [FOCS 2011] is a disperser for varieties over
.
* Ben-Sasson and Kopparty [SIAM J. C 2012] proved that any degree 3 affine
disperser over a prime field is also an affine extractor with related
parameters. Using our structural results, and based on the work of Kaufman and
Lovett [FOCS 2008] and Haramaty and Shpilka [STOC 2010], we generalize this
result to any constant degree
Affine extractors over large fields with exponential error
We describe a construction of explicit affine extractors over large finite
fields with exponentially small error and linear output length. Our
construction relies on a deep theorem of Deligne giving tight estimates for
exponential sums over smooth varieties in high dimensions.Comment: To appear in Comput. Comple
Circuit Size Lower Bounds and #SAT Upper Bounds Through a General Framework
Most of the known lower bounds for binary Boolean circuits with unrestricted depth are proved by the gate elimination method. The most efficient known algorithms for the #SAT problem on binary Boolean circuits use similar case analyses to the ones in gate elimination. Chen and Kabanets recently showed that the known case analyses can also be used to prove average case circuit lower bounds, that is, lower bounds on the size of approximations of an explicit function.
In this paper, we provide a general framework for proving worst/average case lower bounds for circuits and upper bounds for #SAT that is built on ideas of Chen and Kabanets. A proof in such a framework goes as follows. One starts by fixing three parameters: a class of circuits, a circuit complexity measure, and a set of allowed substitutions. The main ingredient of a proof goes as follows: by going through a number of cases, one shows that for any circuit from the given class, one can find an allowed substitution such that the given measure of the circuit reduces by a sufficient amount. This case analysis immediately implies an upper bound for #SAT. To~obtain worst/average case circuit complexity lower bounds one needs to present an explicit construction of a function that is a disperser/extractor for the class of sources defined by the set of substitutions under consideration.
We show that many known proofs (of circuit size lower bounds and upper bounds for #SAT) fall into this framework.
Using this framework, we prove the following new bounds: average case lower bounds of 3.24n and 2.59n for circuits over U_2 and B_2, respectively (though the lower bound for the basis B_2 is given for a quadratic disperser whose explicit construction is not currently known), and faster than 2^n #SAT-algorithms for circuits over U_2 and B_2 of size at most 3.24n and 2.99n, respectively. Here by B_2 we mean the set of all bivariate Boolean functions, and by U_2 the set of all bivariate Boolean functions except for parity and its complement
Extractors for Polynomial Sources over
We explicitly construct the first nontrivial extractors for degree
polynomial sources over . Our extractor requires min-entropy
. Previously, no
constructions were known, even for min-entropy . A key ingredient in
our construction is an input reduction lemma, which allows us to assume that
any polynomial source with min-entropy can be generated by uniformly
random bits.
We also provide strong formal evidence that polynomial sources are unusually
challenging to extract from, by showing that even our most powerful general
purpose extractors cannot handle polynomial sources with min-entropy below
. In more detail, we show that sumset extractors cannot even
disperse from degree polynomial sources with min-entropy . In fact, this impossibility result even holds for a more
specialized family of sources that we introduce, called polynomial
non-oblivious bit-fixing (NOBF) sources. Polynomial NOBF sources are a natural
new family of algebraic sources that lie at the intersection of polynomial and
variety sources, and thus our impossibility result applies to both of these
classical settings. This is especially surprising, since we do have variety
extractors that slightly beat this barrier - implying that sumset extractors
are not a panacea in the world of seedless extraction
Two-Source Dispersers for Polylogarithmic Entropy and Improved Ramsey Graphs
In his 1947 paper that inaugurated the probabilistic method, Erd\H{o}s proved
the existence of -Ramsey graphs on vertices. Matching Erd\H{o}s'
result with a constructive proof is a central problem in combinatorics, that
has gained a significant attention in the literature. The state of the art
result was obtained in the celebrated paper by Barak, Rao, Shaltiel and
Wigderson [Ann. Math'12], who constructed a
-Ramsey graph, for some small universal
constant .
In this work, we significantly improve the result of Barak~\etal and
construct -Ramsey graphs, for some universal constant .
In the language of theoretical computer science, our work resolves the problem
of explicitly constructing two-source dispersers for polylogarithmic entropy
Three-Source Extractors for Polylogarithmic Min-Entropy
We continue the study of constructing explicit extractors for independent
general weak random sources. The ultimate goal is to give a construction that
matches what is given by the probabilistic method --- an extractor for two
independent -bit weak random sources with min-entropy as small as . Previously, the best known result in the two-source case is an
extractor by Bourgain \cite{Bourgain05}, which works for min-entropy ;
and the best known result in the general case is an earlier work of the author
\cite{Li13b}, which gives an extractor for a constant number of independent
sources with min-entropy . However, the constant in the
construction of \cite{Li13b} depends on the hidden constant in the best known
seeded extractor, and can be large; moreover the error in that construction is
only .
In this paper, we make two important improvements over the result in
\cite{Li13b}. First, we construct an explicit extractor for \emph{three}
independent sources on bits with min-entropy .
In fact, our extractor works for one independent source with poly-logarithmic
min-entropy and another independent block source with two blocks each having
poly-logarithmic min-entropy. Thus, our result is nearly optimal, and the next
step would be to break the barrier in two-source extractors. Second, we
improve the error of the extractor from to
, which is almost optimal and crucial for cryptographic
applications. Some of the techniques developed here may be of independent
interests
Deterministic Extractors for Additive Sources
We propose a new model of a weakly random source that admits randomness
extraction. Our model of additive sources includes such natural sources as
uniform distributions on arithmetic progressions (APs), generalized arithmetic
progressions (GAPs), and Bohr sets, each of which generalizes affine sources.
We give an explicit extractor for additive sources with linear min-entropy over
both and , for large prime , although our
results over require that the source further satisfy a
list-decodability condition. As a corollary, we obtain explicit extractors for
APs, GAPs, and Bohr sources with linear min-entropy, although again our results
over require the list-decodability condition. We further
explore special cases of additive sources. We improve previous constructions of
line sources (affine sources of dimension 1), requiring a field of size linear
in , rather than by Gabizon and Raz. This beats the
non-explicit bound of obtained by the probabilistic method.
We then generalize this result to APs and GAPs
Two Source Extractors for Asymptotically Optimal Entropy, and (Many) More
A long line of work in the past two decades or so established close
connections between several different pseudorandom objects and applications.
These connections essentially show that an asymptotically optimal construction
of one central object will lead to asymptotically optimal solutions to all the
others. However, despite considerable effort, previous works can get close but
still lack one final step to achieve truly asymptotically optimal
constructions.
In this paper we provide the last missing link, thus simultaneously achieving
explicit, asymptotically optimal constructions and solutions for various well
studied extractors and applications, that have been the subjects of long lines
of research. Our results include:
Asymptotically optimal seeded non-malleable extractors, which in turn give
two source extractors for asymptotically optimal min-entropy of ,
explicit constructions of -Ramsey graphs on vertices with , and truly optimal privacy amplification protocols with an active adversary.
Two source non-malleable extractors and affine non-malleable extractors for
some linear min-entropy with exponentially small error, which in turn give the
first explicit construction of non-malleable codes against -split state
tampering and affine tampering with constant rate and \emph{exponentially}
small error.
Explicit extractors for affine sources, sumset sources, interleaved sources,
and small space sources that achieve asymptotically optimal min-entropy of
or (for space sources).
An explicit function that requires strongly linear read once branching
programs of size , which is optimal up to the constant in
. Previously, even for standard read once branching programs, the
best known size lower bound for an explicit function is .Comment: Fixed some minor error
Extractor Lower Bounds, Revisited
We revisit the fundamental problem of determining seed length lower bounds for strong extractors and natural variants thereof. These variants stem from a "change in quantifiers" over the seeds of the extractor: While a strong extractor requires that the average output bias (over all seeds) is small for all input sources with sufficient min-entropy, a somewhere extractor only requires that there exists a seed whose output bias is small. More generally, we study what we call probable extractors, which on input a source with sufficient min-entropy guarantee that a large enough fraction of seeds have small enough associated output bias. Such extractors have played a key role in many constructions of pseudorandom objects, though they are often defined implicitly and have not been studied extensively.
Prior known techniques fail to yield good seed length lower bounds when applied to the variants above. Our novel approach yields significantly improved lower bounds for somewhere and probable extractors. To complement this, we construct a somewhere extractor that implies our lower bound for such functions is tight in the high min-entropy regime. Surprisingly, this means that a random function is far from an optimal somewhere extractor in this regime. The techniques that we develop also yield an alternative, simpler proof of the celebrated optimal lower bound for strong extractors originally due to Radhakrishnan and Ta-Shma (SIAM J. Discrete Math., 2000)
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