High-performance blockchain system for fast certification of manufacturing data

Integration of blockchain systems into industrial applications show promise in increasing security, trust, and transparency along the value-chain during product and process tracking. However, current solutions suffer performance deficiencies, or often disregard legacy devices still in operation. We propose a blockchain system built upon an IoT architecture that is secure, modular, easily scalable, and deployable for fast certification of manufacturing data, compatible with current industrial landscapes. First, the proposed architecture is presented along with elements required to manage network functions. Second, easing integration with existing manufacturing solutions, custom APIs are created and subsequently explained. This grants the platform plug-and-play capabilities, requiring minimal hardware and software configuration for deployment. Lastly, a prototype is designed to validate the solution, concluding the viability of the proposed architecture as a fast and secure certification method of manufacturing data.

Feature partitioning for robust tree ensembles and their certification in adversarial scenarios

Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker. We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest in the presence of attacks on a given dataset avoiding the costly computation of evasion attacks.Experimental evaluation on publicly available datasets shows that the proposed feature partitioning strategy provides a significant accuracy improvement with respect to competitor algorithms and that the proposed certification method allows ones to accurately estimate the effectiveness of a classifier where the brute-force approach would be unfeasible.

Hybrid quantum random number generator for cryptographic algorithms

The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.

Novel Certification Method for Quantum Random Number Generators

Random numbers have many uses, but finding true randomness is incredibly difficult. Therefore, quantum mechanics is used, using the essentially unpredictable behavior of a photon, to generate truly random numbers that form the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the performance of our device and the quality of the random bits generated. Self-testing as well as device independent quantum random number generation methods are analyzed in the paper. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. The method is very important for computer science, because it combines the best features from selftesting and device independent methods. It can be used, when the random numbers’ entropy depends on the device and when it does not. In the related researches, these approaches are offered to be used separately, depending on the random number generator. The offered novel certification technology can be properly used, when the device is compromised or spoiled. The technology can successfully detect unintended irregularities, operational problems, abnormalities and problems in the randomization process. The offered mythology assists to eliminate problems related to physical devices. The offered system has the higher certification randomness security and is faster than self-testing approaches. The method is rather efficient because it implements the different certification approaches in the parallel threads. The offered techniques make the offered research must more efficient than the other existing approaches. The corresponding programming simulation is implemented by means of the simulation techniques.

Progress in Certifying Hardware Model Checking Results

We present a formal framework to certify k-induction-based model checking results. The key idea is the notion of a k-witness circuit which simulates the given circuit and has a simple inductive invariant serving as proof certificate. Our approach allows to check proofs with an independent proof checker by reducing the certification problem to pure SAT checks and checking a simple QBF with one quantifier alternation. We also present Certifaiger, the resulting certification toolkit, and evaluate it on instances from the hardware model checking competition. Our experiments show the practical use of our certification method.

Association and correlation between stress, musculoskeletal pain and resilience in nurses before hospital accreditation maintenance assessment

Objective: to associate and correlate musculoskeletal pain, stress and resilience of nurses in the maintenance of Hospital Accreditation Certification. Method: longitudinal study in two moments, before and after the Accreditation maintenance visit, March and June 2019, with 53 nurses from a hospital institution. The data collected was: sociodemographic, clinical and occupational variables, stress, osteomuscular pain and resilience. Descriptive variables, Chi-square test, t test, Fisher’s exact test, Pearson’s correlation and Spearman’s correlation coefficient were used. Results: most of the study participants had average stress levels before and after the evaluation. Most of those who reported pain were at medium stress levels at both times. The resilience capacity increased after the evaluation, which demonstrates that the experienced stressors were adequately addressed. There was no significant association between the cortisol levels and the perceived stress. Conclusion: occupational stress and musculoskeletal pain were experienced by nurses during the Accreditation processes. It was evident that individuality permeated the perception of stress and resilience allowed to overcome the tensions experienced. The study identified that there is a need for planning and implementation of actions to collaborate with the nurses in the best confrontation, aiming to promote resilience.