Physical layer key generation (PLKG) methods are widely studied in academia due to their independence from trusted third parties and computational efficiency. However, the security of these methods depends on the statistical testing of the post-processed random sequences, which raises concerns about their reliability. While min-entropy is commonly used to evaluate the quality of random sequences, few studies provide explicit min-entropy estimation results. Additionally, existing methods for min-entropy estimation, such as the NIST SP 800-90B (90B) standard and entropy estimators based on deep neural networks (DNNs), have not been adequately validated in the context of PLKG. In this work, we present the first empirical study of min-entropy estimation tailored for PLKG. We systematically analyze five representative key generation schemes using both public and collected datasets. We first experimentally demonstrate the overestimation and underestimation behaviors of 90B estimators on time-varying data and propose a practical strategy for their use. Then, we formally analyze the limitations of DNN-based estimators and introduce a Bidirectional Temporal Training and Testing strategy to improve estimation accuracy. Furthermore, we provide theoretical and empirical insights into how preprocessing and quantization affect entropy. Our results show that PLKG sequences often exhibit significant local predictability, leading to lower-than-expected entropy values. These findings highlight previously overlooked security risks and provide practical guidance for designing more robust PLKG systems.

Prediction-based min-entropy estimation methods, also known as predictors, are essential tools for assessing the security of entropy sources. As recommended in NIST SP 800-90B (90B), these methods estimate min-entropy by forecasting the outputs of entropy sources. Owing to their computational efficiency, considerable research has focused on enhancing the accuracy of predictors, including approaches based on deep neural networks (DNNs). However, concerns remain about their interpretability, reliability, and applicability, particularly for DNN-based predictors. In this paper, we first identify key deficiencies in existing prediction-based methods, including those in 90B and DNN-based predictors, which lead to unreliable estimates and poor adaptability across diverse entropy sources. To improve reliability, we model the predictor output distribution and revise the local predictability metric to produce more stable estimates with associated confidence levels. To enhance the interpretability of DNN-based predictors in entropy estimation, we provide the first theoretical analysis linking neural network optimization objectives to min-entropy, clarifying the suitability and learnability of different architectures. We further reveal the inapplicability of existing methods under time-varying sources and propose a new estimation framework that combines online learning, change detection, and Bayesian optimization for dynamic model updates. The experimental results demonstrate that our methods surpass existing approaches in terms of reliability and applicability, especially when dealing with time-varying sources.

High-quality random numbers are essential to communication security, as one of the most important components of cryptographic modules in secure communication protocols. Compared with statistical tests, entropy estimation is considered to be a more essential method for evaluating the quality of random numbers. Prediction-based min-entropy estimators have been proposed and developed as an important branch of randomness testing tools, represented by the predictors included in NIST SP 800-90B (90B) and the estimators based on neural network (NN) in literature. The 90B methods are efficient but suffer inaccuracy in handling complex entropy sources. By contrast, NN-based methods have relatively high prediction accuracy, but the execution cost is too heavy to be implemented as an online test. This paper proposes a decision tree (DT) based entropy estimator, as well as two enhancement techniques: tailored pruning and correlation determination, to tackle the problems in existing approaches. We conduct experiments on various types of entropy sources for evaluation and comparison. The results show that the proposed DT-based estimator achieves prediction accuracy comparable to the state of the art NN-based estimator while reducing the execution time by 99.3%, making it as efficient as 90B predictors. In terms of estimation accuracy, DT-based estimator shows more powerful predictive capability than 90B predictors for complex entropy sources. Particularly, for second-order Markov entropy sources, the prediction accuracy outperforms 90B by 34.6% and TPA-LSTM by 48.7%. We believe that our proposed DT-based estimator can be an important supplement to existing standard entropy estimation methods.

Random number generators (RNGs) play a vital role in cryptographic applications, and ensuring the quality of the generated random numbers is crucial. At the same time, on-the-fly test plays an important role in cryptography because it is used to assess the quality of the sequences generated by entropy sources and to raise an alert when failures are detected. Moreover, environmental noise, changes in physical equipment, and other factors can introduce variations into the sequence, leading to time-varying sequences. This phenomenon is quite common in real-world scenarios, and it needs on-the-fly test. However, in terms of speed and accuracy, current methods based on mathematical formulas or deep learning algorithms for evaluating min-entropy both fail to meet the requirements of on-the-fly test. Therefore, this paper introduces a new estimator specifically designed for on-the-fly min-entropy estimation. To accurately evaluate time-varying data, we employ an appropriate change detection technology. Additionally, we introduce a new calculation method to replace the original global prediction probability calculation approach for accuracy. We evaluate the performance of our estimator using various kinds of simulated datasets, and compare our estimator with other estimators. The proposed estimator effectively meets the requirements of on-the-fly test.

Kyber, selected as the next-generation standard for key encapsulation mechanism in the third round of the NIST post-quantum cryptography standardization process, has naturally raised concerns regarding its resilience against side-channel analysis and other physical attacks. In this paper, we propose a method for profiling the secret key using multiple features extracted based on a binary plaintext-checking oracle. In addition, we incorporate deep learning into the power analysis attack and propose a convolutional neural network suitable for multi-feature recognition. The experimental results demonstrate that our approach achieves an average key recovery success rate of 64.15 running on STM32F429 M4-cortex CPU.

提出一种密码测评工具自动化调度平台方案,该方案能够依据测评工具之间的依赖关系进行自动化装配,调度测评任务有序开展,归集测评中间数据并调度实时数据流转,根据模板输出报告,并支持产品接入、新建系统、系统运行3种测评场景.测评人员只需上传测评对象的应用场景拓扑图,在图中标识检查点选择所要使用的测评工具,然后通过调度平台向测评工具发送调度指令即可完成测评任务.调度平台采用网络接口调度测评工具,具有可扩展性,现有的测评工具只需依据本文提出的测评工具统一接口模型进行适配调整即可集成到调度平台中接受调度.

True Random Number Generator (TRNG) is indispensable in cryptographic algorithms and protocols, and the quality of randomness directly influences the security of cryptographic applications. Multiple theoretical or offline entropy estimation methods have been proposed to evaluate the security of TRNGs, while their ideal assumptions commonly cannot be satisfied due to the perturbation of operating conditions at runtime, which makes it difficult to achieve sufficient entropy for the output of TRNGs in practice. Moreover, the output bitrate of TRNG is another fundamental concern during TRNG practical applications, while popular elementary oscillator-based structure commonly has relatively low output bitrate due to the inherent low sensitivity of entropy extraction to jitter (source of randomness). In this paper, we aim to design a TRNG satisfying both practical security (i.e., on-chip entropy assurance) and high output bitrate simultaneously. In particular, an improved stochastic model and a measurement method are established to quantify the entropy of coherent sampling based TRNG. Moreover, an on-chip entropy assurance module is provided to realize the robustness of the proposed design under various operating conditions. We implement the proposed TRNG in a simulation platform and ASIC chips (with SMIC 130 nm CMOS technology). Experimental results indicate that the generated data has sufficient entropy ( $\geq 0.999$ per bit) under various operating conditions. In addition, all the output can pass the NIST SP800-22 and AIS 31 statistical tests with an output bitrate of 4.2 Mbps, which is equivalent to 2 orders of magnitude faster than that of the elementary oscillator-based TRNG.

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

Renewable energy sources (RESs) are generally connected to the grid through power electronic interfaces, which generate electrical power instantaneously with little inertia. With the increasing penetration of RESs, the grid will gradually develop into a low inertia and underdamped power system, which results in serious grid frequency stabilization problems. The virtual synchronous generator (VSG) is an emerging technology that mimics the operation characteristics of traditional synchronous generators (SGs). Virtual inertia and damping are therefore introduced, which help to stabilize grid frequency. This paper gives a comprehensive overview of the VSG. The basic operation principle of VSG is introduced and analyzed in depth. The key issues related to VSG are summarized and discussed, including hardware configuration, software control strategies, energy supporting methods, and typical applications.

以SiC MOSFET和GaN HEMT为代表的宽禁带电力电子器件代表电力电子期间未来的发展方向,有望在航天和超导储能等低温领域得到应用.为了对两种器件的低温特性有个全面的认识,对SiC MOSFET和GaN HEMT器件的低温特性进行了对比测试和分析.实验结果显示,温度从300 K降至77 K,MOSFET阈值电压增大至2.77倍,而HEMT降低了 42.45％;MOSFET的漏源击穿电压降低了 32.99％,HEMT升高了 20.12％;MOSFET的导通电阻增大至11.42倍,而HEMT降低了 47.34％.基于器件的物理模型,对两种器件低温特性进行分析,分析结果表明,导致两种器件性能不同的原因主要是SiC MOSFET较差的界面品质和HEMT独特的二维电子气结构.

PID control is the most mature control method in classical control theory. PID control is widely used in the electromagnetic bearing control system. However, how to design PID control parameters to reduce the adjustment time and overshoot of electromagnetic bearings after disturbance is still an issue that needs to be further investigated. In this paper, the influence of "natural" stiffness and "natural" damping on system stability is evaluated to select the value range of P and D control parameters, and the value range of the I parameter is determined by using the Rouse criterion. Based on the genetic algorithm with a novel fitness function considering both the system phase margin and closed-loop bandwidth, the PID controller parameters are optimized. The simulation results show that the overshoot and the adjustment time of the genetic algorithm optimized PID controller are much smaller than that of the conventional method.

The quantum-resistant attribute is a new design criterion for cryptography algorithms in the era of quantum supremacy. Lattice-based cryptography is proved to be secure against quantum computing. CRYSTALS-Kyber is a lattice-based promising candidate in the post-quantum cryptography standardization process. This paper proposes a high-performance polynomial ring processor for the CRYSTALS-Kyber algorithm. The processor executes optimized polynomial ring arithmetic, which cuts off over 20%/50% on the times of modular multiplication/addition compared with the straightforward implementations. Besides, the forward and inverse Number Theoretic Transform (NTT) reuse the control logic with the help of an efficient configurable butterfly unit to minimize the area of the finite state machine. Further, the underlying dual-column sequential storage scheme breaks the bottleneck of memory accessing. To evaluate the performance, a fully pipelined architecture is implemented on a low-cost FPGA platform. Benefiting from these optimizations, the Kyber1024 processor can perform NTT operation for a 4-dimensional polynomial vector in 17.1 mu s, and it achieves speedup by a factor of 2.1 compared with the state-of-the-art implementation.

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.

True random number generator (TRNG) as one essential hardware primitive is widely used in cryptography, Monte Carlo simulation, and gambling. To evaluate the security of TRNG, the entropy of the TRNG’s output is usually estimated by the stochastic model in theory or measured off-chip after fabrication. However, the sufficiency of entropy is difficult to be guaranteed in practice due to the facts: 1) the inaccuracy of the model-based jitter measurement method; 2) the variations of the chip manufacturing process and operating environments (such as supply voltage and temperature); and 3) malicious attacks. In this work, we design a novel TRNG architecture with on-chip entropy assurance to properly solve practical security problems. In the design, we propose an on-chip entropy estimator for measuring independent jitter to quantify true randomness, which enables continuous monitoring of TRNG at runtime. Furthermore, with the cooperation of the proposed on-chip entropy estimator and a rational self-adaptive mechanism, the designed TRNG can steadily generate bitstreams with sufficient entropy (≥ 0.999 per bit) against PVT variations. We implement the TRNG architecture in FPGAs with different technology nodes (45 and 65 nm) and SMIC 130 nm chips. Experimental results validate that the designed TRNG has an excellent performance in terms of technology independence and environmental robustness. The generated bitstreams pass the NIST SP800-22 and Diehard statistical test suites successfully without any post-processing.

飞轮储能具有高功率密度、高效率和低损耗的特点,在不间断电源和电网调频等领域有广阔的应用前景.飞轮储能轴承起到支撑飞轮重量、降低摩擦阻力的作用,是决定飞轮储能量、充放电效率和使用寿命的关键.结构和控制是飞轮轴承的两个核心关键技术.本文分析了应用于飞轮储能的机械轴承、电磁轴承、高温超导磁悬浮轴承以及混合轴承的结构,并总结了不同轴承飞轮储能的损耗、转速、储能量和承载力等性能参数,指出混合磁轴承性能最优,可以降低飞轮的损耗和提高飞轮的转速.另外归纳了目前应用于电磁轴承系统的控制方法,介绍了PID控制、滑模控制、模型预测控制和神经网络控制和解耦控制在电磁轴承控制方面的应用,并进一步分析了未来飞轮磁悬浮轴承控制技术的发展方向.对各种控制方法的比较分析结果表明:在飞轮转子线性工作范围内PID控制方法能保持系统稳定;在飞轮转子非线性工作区域,滑模控制、模型预测控制和神经网络控制效果更优;而解耦控制进一步提高了飞轮在高转速下的控制精度.本文可为开展飞轮储能轴承结构和控制方法的研究提供参考.

Kyber is a promising candidate in post-quantum cryptography standardization process. In this paper, we propose a targeted optimization strategy and implement a processor for Kyber on FPGAs. By merging the operations, we cut off 29.4% clock cycles for Kyber512 and 33.3% for Kyber1024 compared with the textbook implementations. We utilize Gentlemen-Sande (GS) butterfly to optimize the Number-Theoretic Transform (NTT) implementation. The bottleneck of memory access is broken taking advantage of a dual-column sequential scheme. We further propose a pipeline architecture for better performance. The optimizations help the processor achieve 31684 NTT operations per second using only 477 LUTs, 237 FFs and 1 DSP. Our strategy is at least 3x more efficient than the state-of-the-art module for NTT with a similar security level.

Mobile devices, such as smart phones, have recently become the typical computing platforms for many users. Consequently, in practice more and more multi-party computation systems are deployed on users’ mobile devices, resulting in various applications such as mobile outsourcing computing and mobile cooperative computing. However, as the mobile platforms may have inherent flaws, the connection of mobile devices and multi-party computation systems usually arouse new security risks. We point out that an application in one party’s mobile device can be a powerful privileged attacker to the multi-party computation system. Previous studies have mainly focused on avoiding the privacy leaks of one or several malicious parties or eavesdroppers on the Internet. This paper presents a privacy enhancing scheme for a kind of secure multi-party computation systems. The scheme can resist the privileged attackers from the party’s mobile device. Our scheme transforms the original computation process and puts the critical calculation process into trusted execution environment. We provide three components to build a privacy-enhanced multi-party computation system with our scheme. Our scheme is implemented to an actual secure multi-party computation system to demonstrate its validity and acceptable performance overhead.

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. The 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. The 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© 2019 John Wiley & Sons, Ltd.

随机数发生器(random number generator,RNG)在现代密码学中处于基础而核心的地位,其生成的随机数为密码算法和安全协议等众多密码应用提供基本安全保障.随着移动互联网、物联网等技术的快速发展,传统纯硬件形式的随机数发生器存在硬件更新困难、开发成本高等问题,导致适用范围受限.因此,在计算机、移动终端等设备上通常采用软件随机数发生器(software RNG,SRNG)提供随机数服务.目前,Linux、Android、iOS以及Windows等典型操作系统平台均具备各自的SRNG,提供基于软件的随机数服务.现有的研究工作主要聚焦在熵源熵不足和后处理模块内部状态泄露问题,这是影响SRNG的随机数服务质量的主要问题.为此,设计并实现一种带有熵监控功能的软件随机数发生器(entropy monitoring SRNG,EM-SRNG)架构,该设计利用高精度的纳秒级系统时钟作为非物理熵源.在线的熵监控模块可实现在发生器运行时对未处理数据的熵进行持续检测,并在熵不足的情况下按需调用后处理模块以改善数据的统计特性.另外,EM-SRNG的后处理模块可选用基于SM3和SM4密码算法设计的两种后处理扩展算法,以保证发生器内部状态的前向/后向安全性.通过对所设计的EM-SRNG与Linux随机数发生器(LRNG,目前主流的软件随机数发生器之一)进行对比分析,实验结果表明:在安全性方面,经SP 800-90B测试后发现EM-SRNG的输出质量与LRNG的dev/random提供的数据质量相当,而略好于LRNG的dev/urandom提供的数据质量,每比特的最小熵约为0.94/bit;在速率方面,EM-SRNG的数据产生速率比LRNG的dev/random高4个数量级左右,但由于在结构中嵌入了基于90B统计套件进行在线熵估计,使得EM-SRNG的速率比LRNG的dev/urandom要慢一些,约为4 Mbps.

Random number generator (RNG) is the basic primitive in cryptography. The randomness of random numbers generated by RNGs is the base of the security of various cryptosystems implemented in network and communications. With the popularization of smart mobile devices (such as smartphones) and the surge in demand for cryptographic applications of such devices, research on providing random number services for mobile devices has attracted more and more attentions. As the important components of smartphones, sensors are used to collect data from user behaviors and environments, and some data sources have the non-deterministic properties. Currently, some work focuses on how to design sensor-based RNG towards smartphones, since no additional hardware is required by this method. It is critical to evaluate the quality of entropy sources which is the main source of randomness for RNGs. However, as far as we know, there is no work to systematically analyze the feasibility for utilizing the raw sensor data to generate random sequences, and how much the entropy contained in the data is. In this paper, we aim to providing an analysis method for quantifying the entropy in the raw data captured by sensors embedded in smartphones, and studying the feasibility of generating random numbers from the data. We establish several data collection models for some typical sensors with different scenarios and data sampling frequencies. Furthermore, we propose a universal entropy estimation scheme for multivariate data to quantify the entropy of the sensor data, and apply it on a type of Android smartphones. The experiments demonstrate that the raw data collected by the sensors has a considerable amount of entropy, and the ability of different sensors to provide entropy has a certain relationship with the usage scenarios of smartphones and the sampling frequency of sensor data. Particularly, when in a static scenario and the sampling frequency is 50Hz, we get a conservative entropy estimation for our testing smartphones based on the min-entropy, which is about 189bits/s, 13bits/s and 254bits/s for the accelerometer, gyroscope, and magnetometer respectively. While the randomness of sensor data in dynamic scenarios will increase compared to static scenarios, because the environment and the way that the user uses the smartphones actually exist differences each time, parts of which are unknowable to the attacker.