Lecture

Random Number Generation

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In course

COM-622: Topics in information-theoretic cryptography

Information-theoretic methods and their application to secrecy & privacy. Perfect information-theoretic secrecy. Randomness extraction & privacy amplification. Secret key generation from common randomness. Measures of information leakage incl. differential ...

Description

This lecture covers the secret communication problem, cryptanalysis, and the generation of random numbers using physical methods and arithmetical processes. It delves into the challenges of producing random digits and real numbers, emphasizing the importance of randomness in high-speed computation. The discussion includes insights from John von Neumann on the limitations of arithmetical methods for generating random numbers and the preference for physical mechanisms. The lecture also addresses the secret key generation problem, focusing on randomness extraction and secret key agreement.

Instructor

Yanina Yurina Shkel

Teaching & PhD PhD Students Serhat Emre Coban, Anuj Kumar Yadav, Cemre Çadir Courses Advanced probability and applications COM-417 In this course, various aspects of probability theory are considered. The first part covers the main theorems in the field (law of large numbers, central limit theorem), while the second part focuses on the theory of martingales and concentration inequalities. Signal processing COM-202 Signal processing theory and applications: discrete and cont

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Related concepts (14)

Random number generation

Random number generation is a process by which, often by means of a random number generator (RNG), a sequence of numbers or symbols that cannot be reasonably predicted better than by random chance is generated. This means that the particular outcome sequence will contain some patterns detectable in hindsight but unpredictable to foresight. True random number generators can be hardware random-number generators (HRNGs), wherein each generation is a function of the current value of a physical environment's attribute that is constantly changing in a manner that is practically impossible to model.

Pseudorandom number generator

A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by an initial value, called the PRNG's seed (which may include truly random values).

Hardware random number generator

In computing, a hardware random number generator (HRNG), true random number generator (TRNG) or non-deterministic random bit generator (NRBG) is a device that generates random numbers from a physical process capable of producing entropy (in other words, the device always has access to a physical entropy source), rather than by means of an algorithm. Such devices are often based on microscopic phenomena that generate low-level, statistically random "noise" signals, such as thermal noise, the photoelectric effect, involving a beam splitter, and other quantum phenomena.

Cryptanalysis

Cryptanalysis (from the Greek kryptós, "hidden", and analýein, "to analyze") refers to the process of analyzing information systems in order to understand hidden aspects of the systems. Cryptanalysis is used to breach cryptographic security systems and gain access to the contents of encrypted messages, even if the cryptographic key is unknown. In addition to mathematical analysis of cryptographic algorithms, cryptanalysis includes the study of side-channel attacks that do not target weaknesses in the cryptographic algorithms themselves, but instead exploit weaknesses in their implementation.

Differential cryptanalysis

Differential cryptanalysis is a general form of cryptanalysis applicable primarily to block ciphers, but also to stream ciphers and cryptographic hash functions. In the broadest sense, it is the study of how differences in information input can affect the resultant difference at the output. In the case of a block cipher, it refers to a set of techniques for tracing differences through the network of transformation, discovering where the cipher exhibits non-random behavior, and exploiting such properties to recover the secret key (cryptography key).

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