Entropy Evaluation for Oscillator-Based True Random Number Generators

Ma, Yuan; Lin, Jingqiang; Chen, Tianyu; Xu, Chen; Liu, Zongbin; Jing, Jiwu

doi:10.1007/978-3-662-44709-3_30

Cited by 29 publications

(22 citation statements)

References 15 publications

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“…in [14] mitigate this problem by assuming that the resulting distribution is Gaussian. This assumption reduces the stochastic model [16]. However, our results show that the approximation used by Yang et.…”

Section: B Entropy Rate Optimisationmentioning

confidence: 74%

A Highly-Portable True Random Number Generator Based on Coherent Sampling

Peetermans

Rožić

Verbauwhede

2019

2019 29th International Conference on Field Programmable Logic and Applications (FPL)

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True Random Number Generators (TRNGs) are indispensable in modern cryptosystems. Unfortunately, in order to guarantee high entropy of the generated numbers, many TRNG designs require a complex implementation procedure, often involving manual placement and routing. In this work, we introduce a dynamic calibration mechanism for the Coherent Sampling Ring Oscillator based TRNG (COSO-TRNG) enabling easy integration of the entropy source into complex systems. The TRNG setup procedure automatically selects a configuration that guarantees the security requirements. In the experiments, we show that the proposed mechanism is capable of assuring correct TRNG operation even when an automatic placement is carried out and when the design is ported to another FPGA family. We generated random bits on both a Xilinx Spartan 6 and a Microsemi SmartFusion2 implementation that, without post processing, passed AIS-31 statistical tests at a throughput of 3.30 Mbit/s and 1.47 Mbit/s respectively.

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Section: B Entropy Rate Optimisationmentioning

confidence: 74%

A Highly-Portable True Random Number Generator Based on Coherent Sampling

Peetermans

Rožić

Verbauwhede

2019

2019 29th International Conference on Field Programmable Logic and Applications (FPL)

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“…. , 16 { } (k is the maximum step of correlation). Perhaps, we could set the parameter of the MultiMMC predictor as a greater range to achieve a more accurate estimated result for the higher stage, but the time complexity grows exponentially with the parameter k as we analyzed in Section 3.3.2.…”

Section: Comparison On Accuracymentioning

confidence: 99%

“…Nowadays, there are two ways to implement entropy estimation: theoretical entropy estimation (stochastic model) and statistical entropy estimation. A theoretical proof for the security of RNGs can be achieved from a suitable stochastic model, as in [14][15][16][17]. But the modeling is always difficult and complex, because it is always based on the specific structure of a RNG and an appropriate assumption on the entropy source's behavior, and even some structures of RNGs still do not have a suitable model [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators

Chen

Zhu

et al. 2020

Security and Communication Networks

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Random number generator (RNG) is a fundamental and important cryptographic element, which has made an outstanding contribution to guaranteeing the network and communication security of cryptographic applications in the Internet age. In reality, if the random number used cannot provide sufficient randomness (unpredictability) as expected, these cryptographic applications are vulnerable to security threats and cause system crashes. Min-entropy is one of the approaches that are usually employed to quantify the unpredictability. e NIST Special Publication 800-90B adopts the concept of min-entropy in the design of its statistical entropy estimation methods, and the predictive model-based estimators added in the second draft of this standard effectively improve the overall capability of the test suite. However, these predictors have problems on limited application scope and high computational complexity, e.g., they have shortfalls in evaluating random numbers with long dependence and multivariate due to the huge time complexity (i.e., high-order polynomial time complexity). Fortunately, there has been increasing attention to using neural networks to model and forecast time series, and random numbers are also a type of time series. In our work, we propose several new and efficient approaches for min-entropy estimation by using neural network technologies and design a novel execution strategy for the proposed entropy estimation to make it applicable to the validation of both stationary and nonstationary sources. Compared with the 90B's predictors officially published in 2018, the experimental results on various simulated and real-world data sources demonstrate that our predictors have a better performance on the accuracy, scope of applicability, and execution efficiency. e average execution efficiency of our predictors can be up to 10 times higher than that of the 90B's for 10 6 sample size with different sample spaces. Furthermore, when the sample space is over 2 2 and the sample size is over 10 8 , the 90B's predictors cannot give estimated results. Instead, our predictors can still provide accurate results. Copyright©

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“…The simplest on-chip method for jitter evaluation consists of counting the toggles of the ring oscillator output over a fixed amount of time and computing the jitter based on the variance of the obtained results. This methodology is used in [15] and [16]. We will refer to this technique as the counter methodology.…”

Section: B Intel Fpgamentioning

confidence: 99%

On-chip jitter measurement for true random number generators

Yang

Rožić

Grujić

et al. 2017

2017 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

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Applications of true random number generators (TRNGs) span from art to numerical computing and system security. In cryptographic applications, TRNGs are used for generating new keys, nonces and masks. For this reason, a TRNG is an essential building block and often a point of failure for embedded security systems. One type of primitives that are widely used as source of randomness are ring oscillators. For a ring-oscillator-based TRNG, the true randomness originates from its timing jitter. Therefore, determining the jitter strength is essential to estimate the quality of a TRNG. In this paper, we propose a method to measure the jitter strength of a ring oscillator implemented on an FPGA. The fast tapped delay chain is utilized to perform the on-chip measurement with a high resolution. The proposed method is implemented on both a Xilinx FPGA and an Intel FPGA. Fast carry logic components on different FPGAs are used to implement the fast delay line. This carry logic component is designed to be fast and has dedicated routing, which enables a precise measurement. The differential structure of the delay chain is used to thwart the influence of undesirable noise from the measurement. The proposed methodology can be applied to other FPGA families and ASIC designs.

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Entropy Evaluation for Oscillator-Based True Random Number Generators

Cited by 29 publications

References 15 publications

A Highly-Portable True Random Number Generator Based on Coherent Sampling

A Highly-Portable True Random Number Generator Based on Coherent Sampling

High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators

On-chip jitter measurement for true random number generators

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