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A long line of work in the past two decades or so established close connections between several different pseudorandom objects and applications, including seeded or seedless non-malleable extractors, two source extractors, (bipartite) Ramsey graphs, privacy amplification protocols with an active adversary, non-malleable codes and many more. 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: 1. Asymptotically optimal seeded non-malleable extractors, which in turn give two source extractors for asymptotically optimal min-entropy of $$O(\log n)$$, explicit constructions of $$K$$-Ramsey graphs on $$N$$ vertices with $$K=\log^{O(1)} N$$, and truly optimal privacy amplification protocols with an active adversary. 2. 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 $$2$$-split state tampering and affine tampering with constant rate and \emph{exponentially} small error. 3. Explicit extractors for affine sources, sumset sources, interleaved sources, and small space sources that achieve asymptotically optimal min-entropy of $$O(\log n)$ or $$2s+O(\log n)$$ (for space $$s$$ sources). 4. An explicit function that requires strongly linear read once branching programs of size $$2^{n-O(\log n)}$$, which is optimal up to the constant in $$O(\cdot)$$. Previously, even for standard read once branching programs, the best known size lower bound for an explicit function is $$2^{n-O(\log^2 n)}$$.
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Extractors for Polynomial Sources over 𝔽₂
We explicitly construct the first nontrivial extractors for degree d ≥ 2 polynomial sources over 𝔽₂. Our extractor requires min-entropy k ≥ n - (√{log n})/((log log n / d)^{d/2}). Previously, no constructions were known, even for min-entropy k ≥ n-1. A key ingredient in our construction is an input reduction lemma, which allows us to assume that any polynomial source with min-entropy k can be generated by O(k) 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 k ≥ n-o(n). In more detail, we show that sumset extractors cannot even disperse from degree 2 polynomial sources with min-entropy k ≥ n-O(n/log log n). 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.
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- Award ID(s):
- 2045576
- PAR ID:
- 10553900
- Editor(s):
- Guruswami, Venkatesan
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Volume:
- 287
- ISSN:
- 1868-8969
- ISBN:
- 978-3-95977-309-6
- Page Range / eLocation ID:
- 287-287
- Subject(s) / Keyword(s):
- Extractors low-degree polynomials varieties sumset extractors Theory of computation → Expander graphs and randomness extractors
- Format(s):
- Medium: X Size: 24 pages; 913462 bytes Other: application/pdf
- Size(s):
- 24 pages 913462 bytes
- Right(s):
- Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.4230/LIPIcs.ITCS.2024.28
