randomness assumption - ορισμός. Τι είναι το randomness assumption
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Τι (ποιος) είναι randomness assumption - ορισμός

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.
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.
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.

Βικιπαίδεια

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.