Mutual correlation of NIST statistical randomness tests and comparison of theirsensitivities on transformed sequences

Abstract

Random sequences are widely used in many cryptographic applications and hence their generation is oneof the main research areas in cryptography. Statistical randomness tests are introduced to detect the weaknesses ornonrandom characteristics that a sequence under consideration may have. In the literature, there exist various statisticalrandomness tests and test suites, de ned as a collection of tests. An efficient test suite should consist of a number ofuncorrelated statistical tests each of which measures randomness from another point of view. `Being uncorrelated\\' is nota well-de ned or well-understood concept in the literature. In this work, we apply Pearson\\'s correlation test to measurethe correlation between the tests.In addition, we de ne ve new methods for transforming a sequence. Our motivation is to detect those testswhose results are invariant under a certain transformation. To observe the correlation, we use two methods. One is thedirect correlation between the tests and the other is the correlation between the results of a test on the sequence andits transformed form. In light of the observations, we conclude that some of the tests are correlated with each other.Furthermore, we conclude that in designing a reliable and efficient suite we can avoid overpopulating the list of testfunctions by employing transformations together with a reasonable number of statistical test functions.