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What (who) is randomness assumption - definition

ANALYZING A SET OF DATA TO SEE IF IT CAN BE DESCRIBED AS RANDOM (PATTERNLESS)

Randomness tests; Tests of randomness; Test for randomness; Tests for randomness

XDH assumption

External Diffie-Hellman assumption; XDH Assumption

The external Diffie–Hellman (XDH) assumption is a computational hardness assumption used in elliptic curve cryptography. The XDH assumption holds that there exist certain subgroups of elliptic curves which have useful properties for cryptography.

https://en.wikipedia.org/wiki/XDH_assumption

Open-world assumption

FORMAL-LOGIC ASSUMPTION THAT THE TRUTH-VALUE OF A STATEMENT IS INDEPENDENT OF WHETHER IT IS KNOWN BY ANY SINGLE OBSERVER OR AGENT TO BE TRUE

Open World Assumption; Open World assumption; Open world assumption; Open-world semantics; Partial-closed world assumption

In a formal system of logic used for knowledge representation, the open-world assumption is the assumption that the truth value of a statement may be true irrespective of whether or not it is known to be true. It is the opposite of the closed-world assumption, which holds that any statement that is true is also known to be true.

https://en.wikipedia.org/wiki/Open-world_assumption

Decisional Diffie–Hellman assumption

Decision Diffie-Hellman problem; Decisional Diffie-Hellman assumption; DDH assumption

The decisional Diffie–Hellman (DDH) assumption is a computational hardness assumption about a certain problem involving discrete logarithms in cyclic groups. It is used as the basis to prove the security of many cryptographic protocols, most notably the ElGamal and Cramer–Shoup cryptosystems.

https://en.wikipedia.org/wiki/Decisional_Diffie–Hellman_assumption

Wikipedia

Randomness test

A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see if it can be described as random (patternless). In stochastic modeling, as in some computer simulations, the hoped-for randomness of potential input data can be verified, by a formal test for randomness, to show that the data are valid for use in simulation runs. In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.

https://en.wikipedia.org/wiki/Randomness test