New Impossible Differential Characteristic of SPECK64 using MILP

Impossible differential attack is one of powerful methods for analyzing block ciphers. When designing block ciphers, it must be safe for impossible differential attacks. In case of impossible differential attack, the attack starts from finding the impossible differential characteristic. However, in the case of the ARX-based block cipher, these analyzes were difficult due to the addition of modulus. In this paper, we introduce 157 new six-round impossible differential characteristics of ARX-basef block cipher, SPECK64, using Mixed Integer Linear Programming (MILP) base impossible differential characteristic search proposed by Cui [3] etc

Efficient Differential Trail Searching Algorithm for ARX Block Ciphers

In this paper, we suggest an advanced method searching for differential trails of block cipher with ARX structure. We use two techniques to optimize the automatic search algorithm of differential trails suggested by Biryukov et al. and obtain 2~3 times faster results than the previous one when implemented in block cipher SPECK

Biclique Cryptanalysis of Lightweight Block Ciphers PRESENT, Piccolo and LED

In this paper, we evaluate the security of lightweight block ciphers PRESENT, Piccolo and LED against biclique cryptanalysis. To recover the secret key of PRESENT-80/128, our attacks require $2^{79.76}$ full PRESENT-80 encryptions and $2^{127.91}$ full PRESENT-128 encryptions, respectively. Our attacks on Piccolo-80/128 require computational complexities of $2^{79.13}$ and $2^{127.35}$, respectively. The attack on a $29$-round reduced LED-64 needs $2^{63.58}$ 29-round reduced LED-64 encryptions. In the cases of LED-80/96/128, we propose the attacks on two versions. First, to recover the secret key of $45$-round reduced LED-80/96/128, our attacks require computational complexities of $2^{79.45}, 2^{95.45}$ and $2^{127.45}$, respectively. To attack the full version, we require computational complexities of $2^{79.37}, 2^{95.37}$ and $2^{127.37}$, respectively. However, in these cases, we need the full codebook. These results are superior to known biclique cryptanalytic results on them

Study of a Carbon Nanowall Synthesized on an MWCNT-Based Buffer Layer for Improvement of Electrical Properties

We conducted experiments to improve the electrical properties of the CNW (carbon nanowall), which has lower electrical properties than other carbon allotropes such as graphene and CNT (carbon nanotube), and report the results through this article. The carbon nanowall has an amorphous buffer layer, leading to low electrical properties, and MWCNT (multi-walled carbon nanotube) was used as a buffer layer to improve this issue, and then a CNW was grown on it by CVD (chemical vapor deposition). Then, the content of MWCNT was adjusted to 30 µL, 50 µL, and 70 µL to analyze the electrical properties accordingly. Alteration in carrier concentration, carrier mobility and resistivity were observed as electrical properties. Dramatic changes in electrical properties with MWCNT content were identified. The ohmic contact state between the MWCNT-based buffer layer and the CNW was investigated by analysis of the I-V and I-R characteristics and the electrical stability according to the linearity of the curve

High-Precision Bootstrapping for Approximate Homomorphic Encryption by Error Variance Minimization

The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt’17) is one of the most promising homomorphic encryption (HE) schemes as it enables privacy-preserving computing over real (or complex) numbers. It is known that bootstrapping is the most challenging part of the CKKS scheme. Further, homomorphic evaluation of modular reduction is the core of the CKKS bootstrapping. As modular reduction is not represented by the addition and multiplication of complex numbers, approximate polynomials for modular reduction should be used. The best-known techniques (Eurocrypt’21) use a polynomial approximation for trigonometric functions and their composition. However, all the previous methods are based on an indirect approximation, and thus it requires lots of multiplicative depth to achieve high accuracy. This paper proposes a direct polynomial approximation of modular reduction for CKKS bootstrapping, which is optimal in error variance and depth. Further, we propose an efficient algorithm, namely the lazy baby-step giant-step (BSGS) algorithm, to homomorphically evaluate the approximate polynomial, utilizing the lazy relinearization/rescaling technique. The lazy-BSGS reduces the computational complexity by half compared to the ordinary BSGS algorithm. The performance improvement for the CKKS scheme by the proposed algorithm is verified by implementation using HE libraries. The implementation results show that the proposed method has a multiplicative depth of 10 for modular reduction to achieve the state-of-the-art accuracy, while the previous methods have depths of 11 to 12. Moreover, we achieve higher accuracy within a small multiplicative depth, for example, 93-bit within multiplicative depth 11. © 2022, International Association for Cryptologic Research

Effect of GNWs/NiO-WO3/GNWs Heterostructure for NO2 Gas Sensing at Room Temperature

Recently, as air pollution and particulate matter worsen, the importance of a platform that can monitor the air environment is emerging. Especially, among air pollutants, nitrogen dioxide (NO2) is a toxic gas that can not only generate secondary particulate matter, but can also derive numerous toxic gases. To detect such NO2 gas at low concentration, we fabricated a GNWs/NiO-WO3/GNWs heterostructure-based gas sensor using microwave plasma-enhanced chemical vapor deposition (MPECVD) and sputter, and we confirmed the NO2 detection characteristics between 10 and 50 ppm at room temperature. The morphology and carbon lattice characteristics of the sensing layer were investigated using field emission scanning electron microscopy (FESEM) and Raman spectroscopy. In the gas detection measurement, the resistance negative change according to the NO2 gas concentration was recorded. Moreover, it reacted even at low concentrations such as 5–7 ppm, and showed excellent recovery characteristics of more than 98%. Furthermore, it also showed a change in which the reactivity decreased with respect to humidity of 33% and 66%

Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study

Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively