Explicit Designs and Extractors

07/15/2020
by   Eshan Chattopadhyay, et al.
0

We give significantly improved explicit constructions of three related pseudorandom objects. 1. Extremal designs: An (n,r,s)-design, or (n,r,s)-partial Steiner system, is an r-uniform hypergraph over n vertices with pairwise hyperedge intersections of size <s. For all constants r≥ s∈ℕ with r even, we explicitly construct (n,r,s)-designs (G_n)_n∈ℕ with independence number α(G_n)≤ O(n^2(r-s)/r). This gives the first derandomization of a result by Rödl and Šinajová (Random Structures Algorithms, 1994). 2. Extractors for adversarial sources: By combining our designs with leakage-resilient extractors (Chattopadhyay et al., FOCS 2020), we establish a new, simple framework for extracting from adversarial sources of locality 0. As a result, we obtain significantly improved low-error extractors for these sources. For any constant δ>0, we extract from (N,K,n, polylog(n))-adversarial sources of locality 0, given just K≥ N^δ good sources. The previous best result (Chattopadhyay et al., STOC 2020) required K≥ N^1/2+o(1). 3. Extractors for small-space sources: Using a known reduction to adversarial sources, we immediately obtain improved low-error extractors for space s sources over n bits that require entropy k≥ n^1/2+δ· s^1/2-δ, whereas the previous best result (Chattopadhyay et al., STOC 2020) required k≥ n^2/3+δ· s^1/3-δ. On the other hand, using a new reduction from small-space sources to affine sources, we obtain near-optimal extractors for small-space sources in the polynomial error regime. Our extractors require just k≥ s·log^Cn entropy for some constant C, which is an exponential improvement over the previous best result, which required k≥ s^1.1·2^log^0.51n (Chattopadhyay and Li, STOC 2016).

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro