Weizmann Logo
ECCC
Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

Login | Register | Classic Style



REPORTS > DETAIL:

Paper:

TR21-075 | 4th June 2021 01:45

Affine Extractors for Almost Logarithmic Entropy

RSS-Feed




TR21-075
Authors: Eshan Chattopadhyay, Jesse Goodman, Jyun-Jie Liao
Publication: 4th June 2021 02:12
Downloads: 1362
Keywords: 


Abstract:

We give an explicit construction of an affine extractor (over $\mathbb{F}_2$) that works for affine sources on $n$ bits with min-entropy $k \ge~ \log n \cdot (\log \log n)^{1 + o(1)}$. This improves prior work of Li (FOCS'16) that requires min-entropy at least $\mathrm{poly}(\log n)$.

Our construction is based on the framework of using correlation breakers and resilient functions, a paradigm that was also used by Li. On a high level, the key sources of our improvement are based on the following new ingredients: (i) A new construction of an affine somewhere random extractor, that we use in a crucial step instead of a linear seeded extractor (for which optimal constructions are not known) that was used by Li. (ii) A near optimal construction of a correlation breaker for linearly correlated sources. The construction of our correlation breaker takes inspiration from an exciting line of recent work that constructs two-source extractors for near logarithmic min-entropy.



ISSN 1433-8092 | Imprint