How to Test the Randomness from the Wireless Channel for Security?

We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the framework is to quantify channel randomness into bit sequences for key generation. Conducting randomness tests on such bit sequences has been a common practice to provide the confidence to validate whether they are random. Interestingly, despite different settings in the tests, existing studies interpret the results the same: passing tests means that the bit sequences are indeed random. In this paper, we investigate how to properly test the wireless channel randomness to ensure enough security strength and key generation efficiency. In particular, we define an adversary model that leverages the imperfect randomness of the wireless channel to search the generated key, and create a guideline to set up randomness testing and privacy amplification to eliminate security loss and achieve efficient key generation rate. We use theoretical analysis and comprehensive experiments to reveal that common practice misuses randomness testing and privacy amplification: (i) no security insurance of key strength, (ii) low efficiency of key generation rate. After revision by our guideline, security loss can be eliminated and key generation rate can be increased significantly

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

An Evolutionary Approach to the Design of Controllable Cellular Automata Structure for Random Number Generation

Cellular Automata (CA) has been used in pseudorandom number generation over a decade. Recent studies show that two-dimensional (2-d) CA Pseudorandom Number Generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-d) CA PRNGs, but they are more complex to implement in hardware than 1-d CA PRNGs. In this paper, we propose a new class of 1-d CA Controllable Cellular Automata (CCA) without much deviation from the structure simplicity of conventional 1-d CA. We give a general definition of CCA first and then introduce two types of CCA – CCA0 and CCA2. Our initial study on them shows that these two CCA PRNGs have better randomness quality than conventional 1-d CA PRNGs but their randomness is affected by their structures. To find good CCA0/CCA2 structures for pseudorandom number generation, we evolve them using the Evolutionary Multi-Objective Optimization (EMOO) techniques. Three different algorithms are presented in this paper. One makes use of an aggregation function; the other two are based on the Vector Evaluated Genetic Algorithm (VEGA). Evolution results show that these three algorithms all perform well. Applying a set of randomness tests on the evolved CCA PRNGs, we demonstrate that their randomness is better than that of 1-d CA PRNGs and can be comparable to that of two-dimensional CA PRNGs

Mt. Random: Multi-Tiered Randomness Beacons

Many decentralized applications require a common source of randomness that cannot be biased or predicted by any single party. Randomness beacons provide such a functionality, allowing parties to periodically obtain fresh random outputs and verify that they are computed correctly. In this work, we propose Mt. Random, a multi-tiered randomness beacon that combines Publicly Verifiable Secret Sharing (PVSS) and (Threshold) Verifiable Random Function (VRF) techniques in order to provide efficiency/randomness quality trade-offs with security under the standard DDH assumption (in the random oracle model) using only a bulletin board as setup (a requirement for the vast majority of beacons). Each tier provides a constant stream of random outputs offering progressive efficiency vs. quality trade-offs: true uniform randomness is refreshed less frequently than pseudorandomness, which in turn is refreshed less frequently than (bounded) biased randomness. This wide span of efficiency/quality allows for applications to consume random outputs from an optimal point in this trade-off spectrum. In order to achieve these results, we construct two new building blocks of independent interest: GULL, a PVSS-based beacon that preprocesses a large batch of random outputs but allows for gradual release of smaller sub-batches\u27\u27, which is a first in the literature of randomness beacons; and a publicly verifiable and unbiasable protocol for Distributed Key Generation protocol (DKG), which is significantly more efficient than most of previous DKGs secure under standard assumptions and closely matches the efficiency of the currently most efficient biasable DKG protocol. We showcase the efficiency of our novel building blocks and of the Mt. Random beacon via benchmarks made with a prototype implementation

Incremental evolution of cellular automata for random number generation

Cellular automata (CA) have been used in pseudorandom number generation for over a decade. Recent studies show that controllable CA (CCA) can generate better random sequences than conventional one-dimensional (1-d) CA and compete with two-dimensional (2-d) CA. Yet the structural complexity of CCA is higher than that of 1-d PCA. It would be good if CCA can attain good randomness quality with the least structural complexity. In this paper, we evolve PCA/CCA to their lowest complexity level using genetic algorithms (GAs). Meanwhile, the randomness quality and output efficiency of PCA/CCA are also evolved. The evolution process involves two algorithms a multi-objective genetic algorithm (MOGA) and an algorithm for incremental evolution. A set of PCA/CCA are evolved and compared in randomness, complexity, and efficiency. The results show that without any spacing, CCA could generate good random number sequences that could pass DIEHARD. And, to obtain the same randomness quality, the structural complexity of CCA is not higher than that of 1-d CA. Furthermore, the methodology developed could be used to evolve other CA or serve as a yardstick to compare different types of CA

On the design of state-of-the-art pseudorandom number generators by means of genetic programming

Congress on Evolutionary Computation. Portland, EEUU, 19-23 June 2004The design of pseudorandom number generators by means of evolutionary computation is a classical problem. Today, it has been mostly and better accomplished by means of cellular automata and not many proposals, inside or outside this paradigm could claim to be both robust (passing all the statistical tests, including the most demanding ones) and fast, as is the case of the proposal we present here. Furthermore, for obtaining these generators, we use a radical approach, where our fitness function is not at all based in any measure of randomness, as is frequently the case in the literature, but of nonlinearity. Efficiency is assured by using only very efficient operators (both in hardware and software) and by limiting the number of terminals in the genetic programming implementation