Analysis of Parallel Implementation of Pilsung Block Cipher On Graphics Processing Unit

This paper focuses on the GPU implementation of the Pilsung block cipher used in the Red Star 3.0 operating system developed in North Korea. The Pilsung block cipher is designed based on AES. One notable feature of the Pilsung block cipher is that the table calculations required for encryption take longer than the encryption process itself. This paper emphasizes the parallel implementation of the Pilsung block cipher by leveraging the parallel processing capabilities of GPUs and evaluates the performance of the Pilsung block cipher. Techniques for optimization are proposed, including the use of Pinned memory to reduce data transfer time and work distribution between the CPU and GPU. Pinned memory helps optimize data transfer, and work distribution between the CPU and GPU needs to be considered for efficient parallel processing. Performance measurements were performed using the Nvidia GTX 3060 laptop for evaluation, comparing the results of applying Pinned memory usage and work distribution optimization. As a result, optimizing memory transfer costs was found to have a greater impact on performance improvement. When both techniques were applied together, approximately a 1.44 times performance improvement was observed

Parallel Quantum Addition for Korean Block Cipher

Adversaries using quantum computers can employ new attacks on cryptography that are not possible with classical computers. Grover\u27s search algorithm, a well-known quantum algorithm, can reduce the search complexity of $O(2^n)$ to $\sqrt{2^n}$ for symmetric key cryptography using an $n$-bit key. To apply the Grover search algorithm, the target encryption process must be implemented as a quantum circuit. In this paper, we present optimized quantum circuits for Korean block ciphers based on ARX architectures. We adopt the optimal quantum adder and design in parallel way with only a few trade-offs between quantum resources. As a result, we provide a performance improvement of 78\% in LEA, 85\% in HIGHT, and 70\% in CHAM in terms of circuit depth, respectively. Finally, we estimate the cost of the Grover key search for Korean block ciphers and evaluate the post-quantum security based on the criteria presented by NIST

On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problematic for structure from motion and stereo, reflective material poses issues for active sensing, and distances for translucent objects are intricate to measure with existing hardware. Training on inaccurate or corrupt data induces model bias and hampers generalisation capabilities. These effects remain unnoticed if the sensor measurement is considered as ground truth during the evaluation. This paper investigates the effect of sensor errors for the dense 3D vision tasks of depth estimation and reconstruction. We rigorously show the significant impact of sensor characteristics on the learned predictions and notice generalisation issues arising from various technologies in everyday household environments. For evaluation, we introduce a carefully designed dataset\footnote{dataset available at https://github.com/Junggy/HAMMER-dataset} comprising measurements from commodity sensors, namely D-ToF, I-ToF, passive/active stereo, and monocular RGB+P. Our study quantifies the considerable sensor noise impact and paves the way to improved dense vision estimates and targeted data fusion.Comment: Accepted at CVPR 2023, Main Paper + Supp. Mat. arXiv admin note: substantial text overlap with arXiv:2205.0456

Grover on SPEEDY

With the advent of quantum computers, revisiting the security of cryptography has been an active research area in recent years. In this paper, we estimate the cost of applying Grover\u27s algorithm to SPEEDY block cipher. SPEEDY is a family of ultra-low-latency block ciphers presented in CHES\u2721. It is ensured that the key search equipped with Grover\u27s algorithm reduces the $n$-bit security of the block cipher to $\frac{n}{2}$-bit. The issue is how many quantum resources are required for Grover\u27s algorithm to work. NIST estimates the post-quantum security strength for symmetric key cryptography as the cost of Grover key search algorithm. SPEEDY provides 128-bit security or 192-bit security depending on the number of rounds. Based on our estimated cost, we present that increasing the number of rounds is insufficient to satisfy the security against attacks on quantum computers. To the best of our knowledge, this is the first implementation of SPEEDY as a quantum circuit

All the Polynomial Multiplication You Need on RISC-V

Polynomial multiplication is a core operation for public key cryptography, such as pre-quantum cryptography (e.g. elliptic curve cryptography) and post-quantum cryptography (e.g. code-based cryptography and multivariate-based cryptography). For this reason, the efficient and secure implementation of polynomial multiplication has been actively conducted for high availability and security level in application services. In this paper, we present all polynomial multiplication methods on modern 32-bit RISC-V processors. We re-designed expensive implementations of polynomial multiplication on legacy microcontrollers (e.g. 8-bit AVR, 16-bit MSP, and 32-bit ARM) for new instruction sets of 32-bit RISC-V processors. Secondly, we suggest the optimal operand length for each polynomial multiplication on 32-bit RISC-V processors. With this implementation technique and Karatsuba algorithm, we achieved scalable features, which ensures the polynomial multiplication in any operand lengths with reasonably fast performance. Third, we propose instruction set extensions for the optimal implementation of polynomial multiplication on 32-bit RISC-V processors. This new feature introduces significant performance enhancements. Lastly, the proposed implementation is a public domain and following researchers can easily re-produce the result

Simpira Gets Simpler: Optimized Simpira on Microcontrollers

Simpira Permutation is a Permutation design using the AES algorithm. The AES algorithm is the most widely used in the world, and Intel has developed a hardware accelerated AES instruction set (AES-NI) to improve the performance of encryption. By using AES-NI, Simpira can be improved further. However, low-end processors that do not support AES-NI require efficient implementation of Simpira optimization. In this paper, we introduce a optimized implementation of a Simpira Permutation in 8-bit AVR microcontrollers and 32-bit RISC-V processors, that do not support the AES instruction set. We firstly pre-computed round keys and omitted the Addroundkey. Afterward, the MixColumn and InvMixColumn of the final round (i.e. 12-th), which were added unnecessarily due to characteristics of Simpira using AES-NI, were omitted. In the AVR microcontroller, the Addroundkey consists of 16 operations, but it has been optimized by eliminating operations where the value of roundkeys is \texttt{0x00}, omitting Addroundkey to 4 operations. In the RISC-V processor, it is implemented using a same optimization technique of AVR implementation. We have carried out experiments 8-bit ATmega128 microcontroller and 32-bit RISC-V processor, which shows up-to \texttt{5.76$\times$ and 37.01$\times$} better performance enhancement than reference codes for the Simpira Permutation, respectively

SPEEDY on Cortex--M3: Efficient Software Implementation of SPEEDY on ARM Cortex--M3

The SPEEDY block cipher suite announced at CHES 2021 shows excellent hardware performance. However, SPEEDY was not designed to be efficient in software implementations. SPEEDY\u27s 6-bit sbox and bit permutation operations generally do not work efficiently in software. We implemented SPEEDY block cipher by applying the implementation technique of bit slicing. As an implementation technique of bit slicing, SPEEDY can be operated in software very efficiently and can be applied in microcontroller. By calculating the round key in advance, the performance on ARM Cortex-M3 for SPEEDY-5-192, SPEEDY-6-192, and SPEEDY-7-192 are 65.7, 75.25, and 85.16 clock cycles per byte (i.e. cpb), respectively. It showed better performance than AES-128 constant-time implementation and GIFT constant-time implementation in the same platform. Through this, we conclude that SPEEDY can show good performance on embedded environments

K-XMSS and K-SPHINCS+^+:Hash based Signatures with\\Korean Cryptography Algorithms

Hash-Based Signature (HBS) uses a hash function to construct a digital signature scheme, where its security is guaranteed by the collision resistance of the hash function used. To provide sufficient security in the post-quantum environment, the length of hash should be satisfied. Modern HBS can be classified into stateful and stateless schemes. Two representative stateful and stateless HBS are XMSS and SPHINCS$^+$, respectively. In this paper, we propose two HBS schemes: K-XMSS and K-SPHINCS$^+$, which replace internal hash functions of XMSS and SPHINCS$^+$ with Korean cryptography algorithms. K-XMSS is a stateful signature, while K-SPHINCS$^+$ is its stateless counterpart. We also showcase the reference implementation of K-XMSS and K-SPHINCS$^+$ employing LSH and two hash functions based on block ciphers (i.e. CHAM and LEA) as the internal hash function. The reference code is developed as a proof-of-concept, which can be optimized for better performance using advanced implementation techniques (e.g. AVX2 and NEON)

Flood fragility analysis for bridges with multiple failure modes

Bridges are one of the most important infrastructure systems that provide public and economic bases for humankind. It is also widely known that bridges are exposed to a variety of flood-related risk factors such as bridge scour, structural deterioration, and debris accumulation, which can cause structural damage and even failure of bridges through a variety of failure modes. However, flood fragility has not received as much attention as seismic fragility despite the significant amount of damage and costs resulting from flood hazards. There have been few research efforts to estimate the flood fragility of bridges considering various flood-related factors and the corresponding failure modes. Therefore, this study proposes a new approach for bridge flood fragility analysis. To obtain accurate flood fragility estimates, reliability analysis is performed in conjunction with finite element analysis, which can sophisticatedly simulate the structural response of a bridge under a flood by accounting for flood-related risk factors. The proposed approach is applied to a numerical example of an actual bridge in Korea. Flood fragility curves accounting for multiple failure modes, including lack of pier ductility or pile ductility, pier rebar rupture, pile rupture, and deck loss, are derived and presented in this study.ope

Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting