Results 1  10
of
195
TestU01: A C library for empirical testing of random number generators
 ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
, 2007
"... We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several ot ..."
Abstract

Cited by 85 (3 self)
 Add to MetaCart
We introduce TestU01, a software library implemented in the ANSI C language, and offering a collection of utilities for the empirical statistical testing of uniform random number generators (RNGs). It provides general implementations of the classical statistical tests for RNGs, as well as several others tests proposed in the literature, and some original ones. Predefined tests suites for sequences of uniform random numbers over the interval (0, 1) and for bit sequences are available. Tools are also offered to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically. Finally, the library provides various types of generators implemented in generic form, as well as many specific generators proposed in the literature or found in widelyused software. The tests can be applied to instances of the generators predefined in the library, or to userdefined generators, or to streams of random numbers produced by any kind of device or stored in files. Besides introducing TestU01, the paper provides a survey and a classification of statistical tests for RNGs. It also applies batteries of tests to a long list of widely used RNGs.
Digital image steganography: Survey and analysis of current methods
 Journal signal processing, Volume 90, Issue
"... Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of ..."
Abstract

Cited by 59 (0 self)
 Add to MetaCart
(Show Context)
Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography’s ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a stateoftheart review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the objectoriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed. Keywords Digital image steganography; spatial domain; frequency domain; adaptive steganography; security. 1.
Powerup SRAM State as an Identifying Fingerprint and Source of True Random Numbers
 IEEE TRANSACTIONS ON COMPUTERS
"... Intermittentlypowered applications create a need for lowcost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that powerup of SRAM produces a physical fingerprint. We propose a system of F ..."
Abstract

Cited by 53 (6 self)
 Add to MetaCart
(Show Context)
Intermittentlypowered applications create a need for lowcost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that powerup of SRAM produces a physical fingerprint. We propose a system of Fingerprint Extraction and Random Numbers in SRAM (FERNS) that harvests static identity and randomness from existing volatile CMOS memory without requiring any dedicated circuitry. The identity results from manufacturetime physically random device threshold voltage mismatch, and the random numbers result from runtime physically random noise. We use experimental data from high performance SRAM chips and the embedded SRAM of the WISP UHF RFID tag to validate the principles behind FERNS. For the SRAM chip, we demonstrate that 8byte fingerprints can uniquely identify circuits among a population of 5,120 instances, and extrapolate that 24byte fingerprints would uniquely identify all instances ever produced. Using a smaller population, we demonstrate similar identifying ability from the embedded SRAM. In addition to identification, we show that SRAM fingerprints capture noise, enabling true random number generation. We demonstrate that a 512byte SRAM fingerprint contains sufficient entropy to generate 128bit true random numbers, and that the generated numbers pass the NIST tests for runs, approximate entropy and blockfrequency.
Physically unclonable functions: a study on the state of the art and future research directions
 in Towards HardwareIntrinsic Security, Security and Cryptology
, 2010
"... The idea of using intrinsic random physical features to identify objects, systems and people is not new. Fingerprint identification of humans dates at least back to the nineteenth century [20] and led to the field of biometrics. In the eighties and nineties of the twentieth century, random patterns ..."
Abstract

Cited by 45 (4 self)
 Add to MetaCart
(Show Context)
The idea of using intrinsic random physical features to identify objects, systems and people is not new. Fingerprint identification of humans dates at least back to the nineteenth century [20] and led to the field of biometrics. In the eighties and nineties of the twentieth century, random patterns in pa
True Random Number Generator Embedded in Reconfigurable Hardware
 Proceedings of the 4th International Workshop on Cryptographic Hardware and Embedded Systems (CHES 2002), SpringerVerlag, LNCS 2523
, 2002
"... This paper presents a new True Random Number Generator (TRNG) based on an analog PhaseLocked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness  the PLL synthesized low jitter clock signal, ..."
Abstract

Cited by 44 (10 self)
 Add to MetaCart
This paper presents a new True Random Number Generator (TRNG) based on an analog PhaseLocked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness  the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded SystemOnaProgrammableChip (SOPC) cryptographic applications and can provide a good quality true random bitstream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.
An embedded true random number generator for FPGAs
 In ACM FPGA ’04
, 2004
"... Field Programmable Gate Arrays (FPGAs) are an increasingly popular choice of platform for the implementation of cryptographic systems. Until recently, designers using FPGAs had less than optimal choices for a source of truly random bits. In this paper we extend a technique that uses onchip jitter a ..."
Abstract

Cited by 41 (0 self)
 Add to MetaCart
(Show Context)
Field Programmable Gate Arrays (FPGAs) are an increasingly popular choice of platform for the implementation of cryptographic systems. Until recently, designers using FPGAs had less than optimal choices for a source of truly random bits. In this paper we extend a technique that uses onchip jitter and PLLs to a much larger class of FPGAs that do not contain PLLs. Our design uses only the Configurable Logic Blocks (CLBs) common to all FPGAs, and has a selftesting capability. Using the intrinsic jitter contained in digital circuits, we produce random bits at speeds of up to 0.5 Mbits/second with good statistical characteristics. We discuss the engineering challenges of extracting random bits from digital circuits, and we report the results of running standard statistical tests (NIST) on the output generated by our system.
TestU01: A Software Library in ANSI C for Empirical Testing of Random Number Generators
, 2007
"... This document describes the software library TestU01, implemented in the ANSI C language, and offering a collection of utilities for the (empirical) statistical testing of uniform random number generators (RNG). The library implements several types of generators in generic form, as well as many spec ..."
Abstract

Cited by 27 (2 self)
 Add to MetaCart
This document describes the software library TestU01, implemented in the ANSI C language, and offering a collection of utilities for the (empirical) statistical testing of uniform random number generators (RNG). The library implements several types of generators in generic form, as well as many specific generators proposed in the literature or found in widelyused software. It provides general implementations of the classical statistical tests for random number generators, as well as several others proposed in the literature, and some original ones. These tests can be applied to the generators predefined in the library and to userdefined generators. Specific tests suites for either sequences of uniform random numbers in [0, 1] or bit sequences are also available. Basic tools for plotting vectors of points produced by generators are provided as well. Additional software permits one to perform systematic studies of the interaction between a specific test and the structure of the point sets produced by a given family of RNGs. That is, for a given kind of test and a given class of RNGs, to determine how large should be the sample size of the test, as a function of the generator’s period length, before the generator starts to fail the test systematically.
Gaussian random number generators
 ACM Computing Surveys
, 2007
"... Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent ..."
Abstract

Cited by 24 (2 self)
 Add to MetaCart
Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent requirements on the quality of Gaussian random number generators (GRNG). This article describes the algorithms underlying various GRNGs, compares their computational requirements, and examines the quality of the random numbers with emphasis on the behaviour in the tail region of the Gaussian probability density function.
FPGA VENDOR AGNOSTIC TRUE RANDOM NUMBER GENERATOR
"... This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phaselocked loop) or external analog components are required. Our solution is based on a framework for a ..."
Abstract

Cited by 23 (1 self)
 Add to MetaCart
(Show Context)
This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phaselocked loop) or external analog components are required. Our solution is based on a framework for a provable secure true random number generator recently proposed by Sunar, Martin and Stinson. It uses a large amount of ring oscillators with identical ring lengths as a fast noise source – but with some deterministic bits – and eliminates the nonrandom samples by appropriate postprocessing based on resilient functions. This results in a slower bit stream with high entropy. Our FPGA implementation achieves a random bit throughput of more than 2 Mbps, remains fairly compact (needing minimally 110 ring oscillators of 3 inverters) and is highly portable. Key words: true random number generators, ring oscillators, jitter, resilient functions 1.