Abstract
We consider the problem of extracting randomness from sources that are efficiently samplable, in the sense that each output bit of the sampler only depends on some small number d of the random input bits. As our main result, we construct a deterministic extractor that, given any d-local source with min-entropy k on n bits, extracts Ω(k 2/nd) bits that are \(2^{-n^{\Omega(1)}}\)-close to uniform, provided d ≤ o(logn) and k ≥ n 2/3 + γ (for arbitrarily small constants γ > 0). Using our result, we also improve a result of Viola (FOCS 2010), who proved a 1/2 − O(1/logn) statistical distance lower bound for o(logn)-local samplers trying to sample input-output pairs of an explicit boolean function, assuming the samplers use at most n + n 1 − δ random bits for some constant δ > 0. Using a different function, we simultaneously improve the lower bound to \(1/2-2^{-n^{\Omega(1)}}\) and eliminate the restriction on the number of random bits.
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De, A., Watson, T. (2011). Extractors and Lower Bounds for Locally Samplable Sources. In: Goldberg, L.A., Jansen, K., Ravi, R., Rolim, J.D.P. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2011 2011. Lecture Notes in Computer Science, vol 6845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22935-0_41
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DOI: https://doi.org/10.1007/978-3-642-22935-0_41
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