Secure key design approaches using entropy harvesting in wireless sensor network: A survey

Physical layer based security design in wireless sensor networks have gained much importance since the past decade. The various constraints associated with such networks coupled with other factors such as their deployment mainly in remote areas, nature of communication etc. are responsible for development of research works where the focus is secured key generation, extraction, and sharing. Keeping the importance of such works in mind, this survey is undertaken that provides a vivid description of the different mechanisms adopted for securely generating the key as well its randomness extraction and also sharing. This survey work not only concentrates on the more common methods, like received signal strength based but also goes on to describe other uncommon strategies such as accelerometer based. We first discuss the three fundamental steps viz. randomness extraction, key generation and sharing and their importance in physical layer based security design. We then review existing secure key generation, extraction, and sharing mechanisms and also discuss their pros and cons. In addition, we present a comprehensive comparative study of the recent advancements in secure key generation, sharing, and randomness extraction approaches on the basis of adversary, secret bit generation rate, energy efficiency etc. Finally, the survey wraps up with some promising future research directions in this area

D2.1 - Report on Selected TRNG and PUF Principles

This report represents the final version of Deliverable 2.1 of the HECTOR work package WP2. It is a result of discussions and work on Task 2.1 of all HECTOR partners involved in WP2. The aim of the Deliverable 2.1 is to select principles of random number generators (RNGs) and physical unclonable functions (PUFs) that fulfill strict technology, design and security criteria. For example, the selected RNGs must be suitable for implementation in logic devices according to the German AIS20/31 standard. Correspondingly, the selected PUFs must be suitable for applying similar security approach. A standard PUF evaluation approach does not exist, yet, but it should be proposed in the framework of the project. Selected RNGs and PUFs should be then thoroughly evaluated from the point of view of security and the most suitable principles should be implemented in logic devices, such as Field Programmable Logic Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs) during the next phases of the project

Recommendations and illustrations for the evaluation of photonic random number generators

The never-ending quest to improve the security of digital information combined with recent improvements in hardware technology has caused the field of random number generation to undergo a fundamental shift from relying solely on pseudo-random algorithms to employing optical entropy sources. Despite these significant advances on the hardware side, commonly used statistical measures and evaluation practices remain ill-suited to understand or quantify the optical entropy that underlies physical random number generation. We review the state of the art in the evaluation of optical random number generation and recommend a new paradigm: quantifying entropy generation and understanding the physical limits of the optical sources of randomness. In order to do this, we advocate for the separation of the physical entropy source from deterministic post-processing in the evaluation of random number generators and for the explicit consideration of the impact of the measurement and digitization process on the rate of entropy production. We present the Cohen-Procaccia estimate of the entropy rate $h(\epsilon,\tau)$ as one way to do this. In order to provide an illustration of our recommendations, we apply the Cohen-Procaccia estimate as well as the entropy estimates from the new NIST draft standards for physical random number generators to evaluate and compare three common optical entropy sources: single photon time-of-arrival detection, chaotic lasers, and amplified spontaneous emission

The Information Catastrophe

Currently we produce 10 to power 21 digital bits of information annually on Earth. Assuming 20 percent annual growth rate, we estimate that 350 years from now, the number of bits produced will exceed the number of all atoms on Earth, or 10 to power 50. After 250 years, the power required to sustain this digital production will exceed 18.5 TW, or the total planetary power consumption today, and 500 years from now the digital content will account for more than half of the Earths mass, according to the mass energy information equivalence principle. Besides the existing global challenges such as climate, environment, population, food, health, energy and security, our estimates here point to another singularity event for our planet, called the Information Catastrophe.Comment: 4 page

Efficient Programmable Random Variate Generation Accelerator from Sensor Noise

We introduce a method for non-uniform random number generation based on sampling a physical process in a controlled environment. We demonstrate one proof-of-concept implementation of the method that reduces the error of Monte Carlo integration of a univariate Gaussian by 1068 times while doubling the speed of the Monte Carlo simulation. We show that the supply voltage and temperature of the physical process must be controlled to prevent the mean and standard deviation of the random number generator from drifting.Alan Turing Institute award: TU/B/000096 EPSRC grants: EP/N510129/1, EP/R022534/1, EP/V004654/1 and EP/L015889/

Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed

A Self-timed Ring Based True Random Number Generator

International audienceSelf-timed rings are oscillators in which several events can evolve evenly-spaced in time thanks to analog effects inherent to the ring stage structure. One of their interesting features is that they provide precise high-speed multiphase signals. This paper presents a true random number generator that exploits the jitter of events propagating in a self-timed ring with a high entropy. Designs implemented in Altera Cyclone III and Xilinx Virtex 5 devices provide high quality random bit sequences passing FIPS 140-1 and NIST SP 800-22 statistical tests at a high bit rate

ENTROPY ANALYSIS OF DATA COLLECTED FROM INERTIAL MEASUREMENT UNIT OF CYBER-PHYSICAL SYSTEM UNDER NON-DISTURBED CONDITIONS

Nowadays cyber-physical systems are widely used for many purposes. We consider the provision of information security of data channels in such systems. Cryptographic data security approach based on random sequences is commonly used to solve this task. Its reliability depends on quality of random data being used, thus truly random sequences are preferable for application. Truly random data generation is a time-consuming process and it requires entropy sources of physical nature. The goal of the paper presented is to research methods and approaches of collecting random numbers using inertial measurement unit as a part of cyber-physical system. Method. Quality assessment of a binary sequence was carried out during the research by determination of random sequence statistical characteristics.Main Results. Research results have shown up that raw data collected from onboard inertial sensors possess lack of entropy under non-disturbed conditions, therefore an additional post-processing is required. Practical Relevance. The results of the research can be used to obtain random sequences for on board cyber-physical systems equipped with inertial measurement units without the use of additional devices. It is planned to collect data from a flying unmanned aerial system in future to apply extractors and to utilize other methods in order to improve quality of a binary sequenc

Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology. Even the Android pseudo RNG (APRNG) merely builds atop the Linux RNG to produce random numbers. This paper presents an exploratory study into methods of generating random numbers on sensor-equipped mobile and Internet of Things devices. We first perform a data collection study across 37 Android devices to determine two things-how much random data is consumed by modern devices, and which sensors are capable of producing sufficiently random data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs collection of data from on-board sensors and combines them via a lightweight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead