Real life application of Random Number Generator algorithm.pptx

Good ppt on random number generation using cyrptography

1. Real life application of Random Number Generator algorithm By Gourav Sharma Submitted to : Dr. Saurabh Jain

2. RANDOM NUMBER GENERATOR A random number is a number chosen by chance -- i.e., randomly, from a set of numbers. All the numbers in a specified distribution have equal probability of being chosen randomly. A random number occurs in a specified distribution only when two conditions are met: The values are uniformly distributed over a defined interval or set, and it is impossible to predict future values based on past or present ones

3. Type of Random Number Generators: There are two main types of random number generators: pseudo-random and true random: Pseudo random number generator (PRNG): Uses algorithms to produce a sequence of numbers that appear random, but is actually predictable. True random number generator (TRNG): Uses physical processes to generate truly random numbers

4. PRNG Pseudo Random Number Generator(PRNG) refers to an algorithm that uses mathematical formulas to produce sequences of random numbers. PRNGs generate a sequence of numbers approximating the properties of random numbers. A PRNG starts from an arbitrary starting state using a seed state. Many numbers are generated in a short time and can also be reproduced later, if the starting point in the sequence is known. Hence, the numbers are deterministic and efficient.

5. Characters of PRNG Efficient Deterministic Periodic

6. TRNG A true random number generator (TRNG) uses a nondeterministic source to make randomness. Most function by measuring unpredictable natural processes, including pulse detectors of ionizing radiation activities, gas discharge tubes, and leaky capacitors. RNG generates a true random number and is produced generally by hardware method, because the random number produced by TRNG is complex to predict because TRNG is generated based on a physical source that is complex to forecast a random value.

7. Characteristics of TRNG Monotonous Accessible Metastability

8. Difference between PRNG & TRNG

9. Applications of RNG algorithms Random number generator (RNG) algorithms have numerous real-life applications across various fields, Such as:

10. 1) Gaming RNG algorithms are widely used in video games for generating random events, such as enemy spawns, loot drops, or dice rolls in tabletop games. They ensure unpredictability and fairness in gameplay.

11. 2) Cryptographic Security RNG algorithms play a crucial role in generating cryptographic keys and ensuring secure communication over the internet. Secure communication protocols like SSL/TLS rely on RNGs for generating session keys.

12. 3) Simulation and Modeling RNG algorithms are essential in simulations and modeling across various disciplines, such as physics, engineering, and finance. They help simulate random events and variability in systems to study their behavior and make predictions.

13. 4) Monte Carlo Methods RNG algorithms are fundamental in Monte Carlo simulations, a computational technique used in various fields like finance, physics, and engineering for numerical integration, optimization, and solving complex problems by generating random samples.

14. 5) Statistical Analysis: RNG algorithms are used in statistical analysis and hypothesis testing to generate random samples for studying distributions, conducting experiments, and performing simulations to analyze data.

15. 6) Artificial Intelligence and Machine Learning RNG algorithms are used in training machine learning models that involve random initialization of weights, shuffling datasets, and generating random noise for regularization techniques

16. 7) Digital Signal Processing RNG algorithms are employed in generating random signals for testing and analyzing digital signal processing algorithms and systems.

17. 8) Randomized Algorithms Certain algorithms, such as randomized algorithms in computer science, utilize RNGs to introduce randomness into their processes for achieving better performance or solving specific problems efficiently.

18. 9) Genetic Algorithms RNGs are utilized in genetic algorithms, a type of optimization algorithm inspired by the process of natural selection, to introduce variation in the population and explore the search space efficiently.

19. 10) Entertainment and Art RNG algorithms are also used in creative applications such as generating random music compositions, visual art, or procedural content generation in video games.

20. In all these applications, the quality of the RNG algorithm is crucial. It should produce random numbers that are statistically independent and uniformly distributed to ensure the reliability and effectiveness of the system or application utilizing it. The End By: Gourav Sharma (21U03059) IT branch -6 th Sem Submitted to : Dr. Saurabh Jain