Modify Linear Congruent Generator Algorithms Using Inverse Elements of Modulo Multiplication for Randomizing Exams

Linear Congruent Generator randomization results are pretty easy to predict. In addition, improper parameter selection in LGC will make the LCG period predictable, and randomization results will repeat quickly. To overcome the weakness of LCG, researchers have developed LCG and hybrid variants, such...

Full description

Saved in:

Bibliographic Details
Published in:	2022 4th International Conference on Cybernetics and Intelligent System (ICORIS) pp. 1 - 3
Main Authors:	Sujono, Maxrizal, Irawadi, Syafrul, Prayanti, Baiq Desy Aniska
Format:	Conference Proceeding
Language:	English
Published:	IEEE 08.10.2022
Subjects:	Cybernetics Generators Intelligent systems inverse element LCG LCG modification mod m pseudo-random number generation
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Linear Congruent Generator randomization results are pretty easy to predict. In addition, improper parameter selection in LGC will make the LCG period predictable, and randomization results will repeat quickly. To overcome the weakness of LCG, researchers have developed LCG and hybrid variants, such as CLCG, dual CLCD, RSA hybrid, Hybrid ECC and Fibonacci hybrid. Although LCG variants and combinations produce better random results, the computational level is more complex than LCG. In this study, we propose a modification of LCG by adding an inverse element to the multiplication mod m. Like CLCG, this research is an LCG which is continued by calculating the inverse element of the multiplication mod m. The results showed that the resulting modified LCG worked well, and the randomization effect was more random than the original LCG.
DOI:	10.1109/ICORIS56080.2022.10031381