Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Non-Uniform Attacks Against Pseudoentropy (1704.08678v2)

Published 27 Apr 2017 in cs.CR, cs.IT, and math.IT

Abstract: De, Trevisan and Tulsiani [CRYPTO 2010] show that every distribution over $n$-bit strings which has constant statistical distance to uniform (e.g., the output of a pseudorandom generator mapping $n-1$ to $n$ bit strings), can be distinguished from the uniform distribution with advantage $\epsilon$ by a circuit of size $O( 2n\epsilon2)$. We generalize this result, showing that a distribution which has less than $k$ bits of min-entropy, can be distinguished from any distribution with $k$ bits of $\delta$-smooth min-entropy with advantage $\epsilon$ by a circuit of size $O(2k\epsilon2/\delta2)$. As a special case, this implies that any distribution with support at most $2k$ (e.g., the output of a pseudoentropy generator mapping $k$ to $n$ bit strings) can be distinguished from any given distribution with min-entropy $k+1$ with advantage $\epsilon$ by a circuit of size $O(2k\epsilon2)$. Our result thus shows that pseudoentropy distributions face basically the same non-uniform attacks as pseudorandom distributions.

Citations (2)

Summary

We haven't generated a summary for this paper yet.