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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > SAMPLABLE DISTRIBUTIONS:
Reports tagged with Samplable distributions:
TR00-084 | 6th November 2000
Salil Vadhan, Amit Sahai

A Complete Problem for Statistical Zero Knowledge

We present the first complete problem for SZK, the class of (promise)
problems possessing statistical zero-knowledge proofs (against an
honest verifier). The problem, called STATISTICAL DIFFERENCE, is to
decide whether two efficiently samplable distributions are either
statistically close or far apart. This gives a new characterization
of SZK that makes ... more >>>


TR05-145 | 5th December 2005
Ronen Shaltiel

How to get more mileage from randomness extractors

Let $\cal C$ be a class of distributions over $\B^n$. A deterministic randomness extractor for $\cal C$ is a function $E:\B^n \ar \B^m$ such that for any $X$ in $\cal C$ the distribution $E(X)$ is statistically close to the uniform distribution. A long line of research deals with explicit constructions ... more >>>


TR10-091 | 14th May 2010
Nikolay Vereshchagin

An Encoding Invariant Version of Polynomial Time Computable Distributions

When we represent a decision problem,like CIRCUIT-SAT, as a language over the binary alphabet,
we usually do not specify how to encode instances by binary strings.
This relies on the empirical observation that the truth of a statement of the form ``CIRCUIT-SAT belongs to a complexity class $C$''
more >>>


TR23-071 | 8th May 2023
Yuval Filmus, Itai Leigh, Artur Riazanov, Dmitry Sokolov

Sampling and Certifying Symmetric Functions

A circuit $\mathcal{C}$ samples a distribution $\mathbf{X}$ with an error $\epsilon$ if the statistical distance between the output of $\mathcal{C}$ on the uniform input and $\mathbf{X}$ is $\epsilon$. We study the hardness of sampling a uniform distribution over the set of $n$-bit strings of Hamming weight $k$ denoted by $\mathbf{U}^n_k$ ... more >>>


TR24-168 | 3rd November 2024
Ronen Shaltiel

Multiplicative Extractors for Samplable Distributions

Trevisan and Vadhan (FOCS 2000) introduced the notion of (seedless) extractors for samplable distributions as a possible solution to the problem of extracting random keys for cryptographic protocols from weak sources of randomness.
They showed that under a very strong complexity theoretic assumption, there exists a constant $\alpha>0$ such that ... more >>>


TR24-204 | 16th December 2024
Marshall Ball, Ronen Shaltiel, Jad Silbak

Extractors for Samplable Distributions with Low Min-Entropy

Trevisan and Vadhan (FOCS 2000) introduced the notion of (seedless) extractors for samplable distributions. They showed that under a very strong complexity theoretic hardness assumption, there are extractors for samplable distributions with large min-entropy of $k=(1-\gamma) \cdot n$, for some small constant $\gamma>0$. Recent work by Ball, Goldin, Dachman-Soled and ... more >>>




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