Sample(x)=(a*x<=t) is a distinguisher with probability 1/8
Mikkel Thorup

TL;DR
This paper introduces a simple, efficient sampling method using modular multiplication for distinguishers with probability 1/8, improving computational simplicity over previous approaches in streaming and space-limited contexts.
Contribution
It presents a new, computationally friendly distinguisher based on modular multiplication that achieves a probability of 1/8, simplifying previous more complex methods.
Findings
The sampling method is efficient for w-bit integers, including 8, 16, 32, 64 bits.
It achieves a distinguisher probability of 1/8 using simple modular multiplication.
The approach improves computational efficiency over prior methods like Naor and Naor's.
Abstract
A random sampling function Sample:U->{0,1} for a key universe U is a distinguisher with probability p if for any given assignment of values v(x) to the keys x in U, including at least one non-zero v(x)!=0, the sampled sum sum{ v(x) | x in U and Sample(x) } is non-zero with probability at least p. Here the key values may come from any commutative monoid (addition is commutative and associative and zero is neutral). Such distinguishers were introduced by Vazirani [PhD thesis 1986], and Naor and Naor used them for their small bias probability spaces [STOC'90]. Constant probability distinguishers are used for testing in contexts where the key values are not computed directly, yet where the sum is easily computed. A simple example is when we get a stream of key value pairs (x_1,v_1),(x_2,v_2),...,(x_n,v_n) where the same key may appear many times. The accumulated value of key x is…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Cryptography and Residue Arithmetic · Machine Learning and Algorithms
