Secure Storage Research Articles

OPTIMIZING AUTHENTICATION SECURITY IN INTELLIGENT SYSTEMS THROUGH VISUAL BIOMETRICS FOR ENHANCED EFFICIENCY

Context. The primary objective of this article is to explore aspects related to ensuring security and enhancing the efficiency of authentication processes in intelligent systems through the application of visual biometrics. The focus is on advancing and refining authentication systems by employing sophisticated biometric identification methods. Objective. A specialized intelligent system has been developed, utilizing a Siamese neural network to establish secure user authentication within the existing system. Beyond incorporating fundamental security measures such as hashing and secure storage of user credentials, the contemporary significance of implementing two-factor authentication is underscored. This approach significantly fortifies user data protection, thwarting most contemporary hacking methods and safeguarding against data breaches. The study acknowledges certain limitations in its approach, possibly affecting the generalizability of the findings. These limitations provide avenues for future research and exploration, contributing to the ongoing evolution of authentication methodologies in intelligent systems. Method. The two-factor authentication system integrates facial recognition technology, employing visual biometrics for heightened security compared to alternative two-factor authentication methods. Various implementations of the Siamese neural network, utilizing Contrastive loss function and Triplet loss function, were evaluated. Subsequently, a neural network employing the Triplet loss function was implemented and trained. Results. The article emphasizes the practical implications of the developed intelligent system, showcasing its effectiveness in minimizing the risk of unauthorized access to user accounts. The integration of contemporary authentication methodologies ensures a secure and robust user authentication process. Conclusions. The implementation of facial recognition technology in authentication processes has broader social implications. It contributes to a more secure digital environment by preventing unauthorized access, ultimately safeguarding user privacy and data. The study’s originality lies in its innovative approach to authentication, utilizing visual biometrics within a Siamese neural network framework. The developed intelligent system represents a valuable contribution to the field, offering an effective and contemporary solution to user authentication challenges.

Comparative study of password storing using hash function with MD5, SHA1, SHA2, and SHA3 algorithm

<span>The main purpose of passwords is to prevent unauthorized people from accessing the system. The rise in internet users has led to an increase in password hacking, which has resulted in a variety of problems. These issues include opponents stealing a company's or nation's private information and harming the economy or the organization's security. Password hacking is a common tool used by hackers for illegal purposes. Password security against hackers is essential. There are several ways to hack passwords, including traffic interception, social engineering, credential stuffing, and password spraying. In an attempt to prevent hacking, hashing algorithms are therefore mostly employed to hash passwords, making password cracking more difficult. In the suggested work, several hashing techniques, including message digest (MD5), secure hash algorithms (SHA1, SHA2, and SHA3) have been used. They have become vulnerable as a result of being used to store passwords. A rainbow table attack is conceivable. Passwords produced with different hash algorithms can have their hash values attacked with the help of the Hashcat program. It is proven that the SHA3 algorithm can help with more secure password storage when compared to other algorithms.</span>

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Developing a trustworthy national health data space: public needs and policy implications

Abstract Background The ongoing development of a national health data space in Switzerland presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. Central to this development is the Health2030 strategy, which calls for the establishment of public trust in the reliability and security of data collection, storage, and exchange. Methods We conducted four focus groups across the German, French and Italian-speaking regions of Switzerland to inform the development of the Swiss health data space that fosters public trust. To inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication requirements. We synthesized our findings using a thematic analysis approach and proposed a guiding health policy strategy. Results Citizens perceive the health data space as trustworthy conditional on the delivery of its promised higher levels of transparency, security, and lower risk compared to other data sharing initiatives. To ensure its trustworthiness, citizens call for the provision of value-based consent to control data access based on the actors involved and research purposes, alongside implementing accountability measures for data misuse. Governance framework requirements entail establishing an advisory group for the health data space with sufficient representation of the Swiss population, including members of the public, patient advisory groups, and researchers. Public communication requirements call for ongoing monitoring of public opinion on health data space, alongside establishing public engagement initiatives, such as public fora, to inform ongoing progress. Conclusions Policy efforts should prioritize the meaningful involvement of relevant stakeholders throughout all stages of the health data space development to foster trust. Central to these efforts is balancing public expectations with their practical implementation. Key messages • For the health data space to be trustworthy, citizens require transparency on their data use and the security measures taken, value-based consent for data access, and accountability measures. • Diverse representation in governance and continuous public involvement are key policy measures to build public trust in the development of national health data spaces.

Security Protection of Video-GIS Data Based on Data Encryption and Digital Watermarking

Abstract. Video-GIS data, a novel generation of multimedia produced through the integration of geographic videos and GIS, has evolved into an innovative form of geospatial data representation. As a kind of digital products, Video-GIS data is vulnerable to unauthorized replication, dissemination, and usage. The potential for large-scale leakage of sensitive or confidential information, including commercial, political, military, and personal privacy data, poses a significant threat to the rights of data producers and may even compromise national security. Given this context, the primary concern is ensuring the security protection of video data in an open and shared data environment. To address this issue, we propose a novel scheme for the security protection of Video-GIS data in this paper, which collaboratively utilizes digital watermarking and data encryption. In the proposed scheme, Video-GIS data is divided into two categories, i.e., general and confidential, based on the presence or absence of sensitive information within the image frames. Appropriate security measures were meticulously designed for each data type to ensure effective prevention and control. Experimental results indicate that this watermarking algorithm exerts minimal influence on the quality of the Video-GIS data, while simultaneously maintaining optimal invisibility and robustness. The employed audio denoising method is characterized by its low complexity, simplicity, and feasibility, thereby ensuring the security of audio signal storage and transmission. This approach effectively realizes the dual protection objective for Video-GIS data, audio, and video.

Research on Key Technologies of Spatial Domain Image Steganography and Analysis of Typical Applications

In the digital age, secure storage and communication of information are crucial aspects of everyday life, drawing significant global interest. Steganography serves as a pivotal method for concealing private information within various media forms—such as images, videos, and text—enabling secure information transmission. This paper delineates the distinctions between information hiding and encryption and delves into image steganography. The focus here is on spatial-based image steganography techniques, which are broadly categorized into traditional and deep learning-based methods. Traditional methods rely on modifications to the pixel values of the host image, often resulting in minor but detectable changes if not carefully managed. On the other hand, deep learning-based methods leverage neural networks to learn how to encode data in an image in a way that is much harder to detect. A comprehensive performance analysis of these techniques is provided, complete with comparative tables to aid in clarity and reference. Additionally, this paper aims to guide researchers by charting current trends in the field and suggesting future research directions. By exploring these advanced techniques, the paper contributes to the broader discourse on enhancing the security and efficacy of steganographic practices.

Non-volatile and Secure Optical Storage Medium with Multilevel Information Encryption.

Non-volatile photomemory based on photomodulated luminescent materials offers unique advantages over voltage-driven memory, including low residual crosstalk and high storage speed. However, conventional materials have thus far been volatile and insecure for data storage because of low trap depth and single-level storage channels. Therefore, the development of a novel non-volatile multilevel storage medium for data encryption remains a challenge. Herein, a robust, non-volatile, multilevel optical storage medium is reported, based on a photomodulated Ba3MgSi2O8:Eu3+, which combined the merits of light-induced valence (Eu3+ → Eu2+) and photochromic phenomena using optical stimulation effects, accompanied by larger luminescent and color contrasts (>90%). These two unique features provided dual-level storage channels in a single host, significantly improving the data storage security. Notably, dual-level optical signals could be written and erased simultaneously by alternating 265 and 365 nm light stimuli. Theoretical calculations indicated that robust color centers induced by intrinsic interstitial Mg and vacancy defects with suitable trap depths enable excellent reversibility and long-term storage capability. By relying on different luminescent readout mechanisms, the encrypted dual-level information can be accurately decrypted by separately probing the Eu2+ and Eu3+ signals, thus ensuring information security. This study proposes a novel approach for constructing multilevel information storage channels for information security.

Progress in carbon dioxide capture, storage and monitoring in geological landform

Carbon Capture and Storage (CCS) is recognized as a potent strategy for managing the accumulation of human-generated CO2 in the atmosphere, helping to alleviate climate change’s effects. The CO2 gas is captured from the point source through methods such as pre-treating fossil fuels, oxy-fuel combustion, or post-combustion capture; thereafter; it is transported to a storage location and injected into geological formations. This article provides an overview of carbon dioxide capture and sequestration, focusing on its key principles, technologies, associated risks, and challenges. Direct Air Capture (DAC) and Scalable Modelling, Artificial intelligence (Al), Rapid Theoretical calculations SMART technologies are detailed as emerging and promising approaches to CO2 capture. Numerous pilot and commercial projects commissioned to manage carbon dioxide emissions are presented. Additionally, the paper explores approaches combining geological, geophysical, geochemical, and environmental monitoring techniques to ensure the secure and sustainable storage of CO2 underground. These are essential to address uncertainties, minimize risks, and build public confidence in CCS as a viable climate mitigation strategy. The successful deployment of these technologies on a global scale will require continued innovation, particularly in the areas of monitoring, risk management, and public engagement. Emerging technologies such as AI and SMART systems could play a crucial role in enhancing the efficiency and safety of CCS operations. However, the integration of these advancements with existing infrastructure and regulatory frameworks remains a challenge. Ultimately, a multi-disciplinary approach, combining technological, economic, and regulatory perspectives, will be vital to realizing the full potential of CCS in combating climate change.

A secure blockchain framework for healthcare records management systems

AbstractElectronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance. Several problems in the past few decades regarding medical information management have threatened patient information privacy. In intelligent healthcare applications, the privacy of patients' data is one of the main concerns. As a result, blockchain is a severe necessity as it can enhance transparency and security in medical applications. Accordingly, this paper uses the design science method to propose a secure blockchain framework for healthcare records management systems. The proposed framework comprises five components: a blockchain network, smart contracts, privacy key management, data encryption, and integration with healthcare information technology. In the proposed framework, healthcare organizations can manage healthcare information securely and privately. Additionally, a secure storage system for electronic records is proposed to meet these organizations' needs. It provides security and privacy for healthcare organizations, especially when managing healthcare information, and also proposes a secure storage system for electronic records to meet the needs of the organizations.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

Blockchain‐IoT: A revolutionary model for secure data storage and fine‐grained access control in internet of things

AbstractWith the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.

Distributed Ledger-Based Authentication and Authorization of IoT Devices in Federated Environments

One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop a “lightweight” protocol to enable authentication and authorization of IoT devices in federated environments and ensure the secure communication of IoT devices. We propose a novel Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI) which takes advantage of the unique fingerprint of IoT devices based on their configuration and additional hardware modules, such as Physical Unclonable Function, to provide flexible authentication and authorization based on Distributed Ledger technology. Moreover, LAAFFI supports IoT devices with limited computing resources and devices not equipped with secure storage space. We implemented a prototype of LAAFFI and evaluated its performance in the Hyperledger Fabric-based IoT framework. Three main metrics were evaluated: latency, throughput (number of operations or transactions per second), and network resource utilization rate (transmission overhead introduced by the LAAFFI protocol). The performance tests conducted confirmed the high efficiency and suitability of the protocol for federated IoT environments. Also, all LAAFFI components are scalable as confirmed by tests. We formally evaluated LAAFFI security using Verifpal as a formal verification tool. Based on the models developed for Verifpal, we validated their security properties, such as message secrecy, authenticity, and freshness. Our results show that the proposed solution can improve the security of federated IoT environments while providing zero-day interoperability and high scalability. Compared to existing solutions, LAAFFI is more efficient due to the use of symmetric cryptography and algorithms adapted for operations involving IoT devices. LAAFFI supports multiple authorization mechanisms, and since it also offers authentication and accountability, it meets the requirements of Authentication, Authorization and Accounting (AAA). It uses Distributed Ledger (DL) and smart contracts to ensure that the request complies with the policies agreed between the organizations. LAAFFI offers authentication of devices belonging to a single organization and different organizations, with the assurance that the encryption key will be shared with another device only if the appropriate security policy is met. The proposed protocol is particularly useful for ensuring the security of federated IoT environments created ad hoc for special missions, e.g., operations conducted by NATO countries and disaster relief operations Humanitarian Assistance and Disaster Relief (HADR) involving military forces and civilian services, where immediate interoperability is required.

Feasibility study of underground CO2 storage through Water Alternative Gas (WAG) operation: A case study in Southwestern Ontario

Geological CO2 storage (GCS) offers a promising, low-risk, long-term solution for carbon sequestration. This study assesses the feasibility of GCS water-alternating-gas (WAG) operations in a brine formation targeted for the Western Ontario GCS plan. The Cambrian strata shows potential for CO2 storage but faces challenges like limited lateral extension and geopolitical issues from plume migration. To mitigate these risks, a deep evaluation of CO2 trapping mechanisms is crucial. CO2 dissolution into brine provides secure storage by reducing the free plume. The proposed WAG operation promotes dissolution, using a vertical equilibrium model coupled with a black oil model to estimate CO2 trapping proportions during and post-injection. The Taguchi design of experiments investigates various WAG scenarios with minimal simulations, reducing numerical costs. Sensitivity analysis/optimization of WAG parameters, including water extraction rate (WER), water injection rate (WIT), and CO2 injection time (CIT), ranked their effectiveness in CO2 dissolution. Optimal WAG operations can reduce the free plume by 49 % and increase the dissolution ratio from 0.26 to 0.52. CIT and WIT were found to be the most influential factors, contributing 42 % and 31 % to CO2 dissolution, and 43 % and 34 % to overall CO2 trapping, respectively, while WER exhibited a lower impact of 8–9%.

ExamGuard: Smart contracts for secure online test

The education sector is currently experiencing profound changes, primarily driven by the widespread adoption of online platforms for conducting examinations. This paper delves into the utilization of smart contracts as a means to revolutionize the monitoring and execution of online examinations, thereby guaranteeing the traceability of evaluation data and examinee activities. In this context, the integration of advanced technologies such as the PoseNet algorithm, derived from the TensorFlow Model, emerges as a pivotal component. By leveraging PoseNet, the system adeptly identifies both single and multiple faces of examinees, thereby ensuring the authenticity and integrity of examination sessions. Moreover, the incorporation of the COCO dataset facilitates the recognition of objects within examination environments, further bolstering the system's capabilities in monitoring examinee activities.of paramount importance is the secure storage of evidence collected during examinations, a task efficiently accomplished through the implementation of the blockchain technology. This platform not only ensures the immutability of data but also safeguards against potential instances of tampering, thereby upholding the credibility of examination results. Through the utilization of smart contracts, the proposed framework not only streamlines the examination process but also instills transparency and integrity, thereby addressing inherent challenges encountered in traditional examination methods. One of the key advantages of this technological integration lies in its ability to modernize examination procedures while concurrently reinforcing trust and accountability within the educational assessment ecosystem. By harnessing the power of smart contracts, educational institutions can mitigate concerns pertaining to data manipulation and malpractice, thereby fostering a more secure and reliable examination environment. Furthermore, the transparency afforded by blockchain technology ensures that examination outcomes are verifiable and auditable, instilling confidence among stakeholders and enhancing the overall credibility of the assessment process. In conclusion, the adoption of smart contracts represents a paradigm shift in the realm of educational assessment, offering a comprehensive solution to the challenges posed by traditional examination methods. By embracing advanced technologies such as PoseNet and blockchain, educational institutions can not only streamline examination procedures but also uphold the highest standards of integrity and accountability. As such, the integration of smart contracts holds immense potential in shaping the future of online examinations, paving the way for a more efficient, transparent, and trustworthy assessment ecosystem.

Accelerating Linear Congruential Generators with Carbon Nanotube Field-Effect Transistors

Abstract In the era of technological advancements, safeguarding information privacy has become paramount across sectors like defense, finance, healthcare, and more. At the heart of these security measures lies cryptography, a field that necessitates the efficient processing and secure storage of data. Our project focuses on the optimization of Linear Congruential Generators (LCG), pivotal for cryptographic applications and pseudorandom number generation. Here it has been pioneered the implementation of LCG using Carbon Nanotube Field-Effect Transistor (CNTFET) technology, targeting the reduction of leakage power through the innovative application of sleep techniques. This advanced approach not only embodies a leap towards minimizing energy consumption but also enhances the overall performance and reliability of cryptographic systems. By integrating sleep modes into the CNTFET-based LCG design, our methodology significantly curtails leakage power without compromising the generator’s efficiency or output quality. The successful implementation and synthesis of this design, employing cutting-edge CNTFET technology, underscore the potential for substantial improvements in power efficiency while maintaining high standards of security and randomness essential for cryptographic algorithms.

PROBLEMATIC ASPECTS OF DEVELOPING APPLICATIONS FOR GENERATING AND STORING USER PASSWORDS

In today’s digital world, the security of personal data is becoming an increasingly pressing issue. Apps for storing user passwords are an important tool in ensuring the security of online accounts, but their development is not without problematic aspects. This article discusses the key challenges developers face when building these applications. Particular attention is paid to storage security, authentication mechanisms, as well as usability and integration with other services. By analyzing these issues, you can identify the main challenges and find ways to solve them, contributing to the creation of more reliable and user- friendly applications for storing user passwords. This article provides an overview of the challenges that arise when developing applications to store user passwords and provides recommendations for solving them. The article discusses such important aspects as data protection, prevention of cyberattacks, compliance with security standards, and usability for the end user. Understanding these challenges and being able to solve them plays a key role in creating trustworthy and effective applications that can provide a high level of security and convenience for users in their everyday online experience. The article describes the practical development of an application for generating complex passwords, as well as their further secure storage in an encrypted database with subsequent integration with third-party applications for more secure storage. The article is of great practical significance. Keywords: generating passwords, storing passwords, cryptography, safety, encryption.

Techno-economic-environmental study of CO2 and aqueous formate solution injection for geologic carbon storage and enhanced oil recovery

As carbon capture, utilization, and storage (CCUS), carbon-dioxide enhanced oil recovery (CO2 EOR) has inherent shortcomings, such as inefficient oil recovery and carbon storage, and low storage security with mobile CO2. This paper presents a techno-economic-environmental analysis of using formate species, a product of CO2 electrochemical reduction, as an alternative carbon carrier for sequestration and EOR in a carbonate oil reservoir in the Gulf of Mexico Basin. CO2 injection, water-alternating-CO2 injection, and aqueous formate solution injection were compared using a compositional reservoir simulation model and an economic calculator. Formate solution injection yielded greater levels of oil recovery and net carbon storage, where the carbon-bearing species resided in the dense aqueous phase without having to rely on petrophysical trapping mechanisms (structural and capillary). The enhanced oil production, net carbon storage, and storage security can be promoted by providing formate-based CCUS with more incentives (e.g., greater tax credit) in comparison to CO2-based CCUS for EOR and the manufacture of chemicals and products. In establishing carbon storage incentive policies and regulations, policymakers should include alternative carbon carriers.

Effects of Aqueous Solubility and Geochemistry on CO2 Storage in Offshore Basins

The increasing global focus on carbon capture and storage (CCS) has highlighted the potential for offshore CO2 sequestration, particularly following recent successes in onshore projects. This research investigates the qualitative analysis of carbon trapping efficiency in offshore basins, employing a GEM simulator to incorporate factors such as aqueous solubility and geochemistry. The findings reveal that anticlines represent ideal geological structures for carbon storage, effectively trapping a significant portion of injected CO2. For effective mineralization, it is crucial to dissolve CO2 into saline aquifers to generate H+, which facilitates the release of Ca2+ and Al3+ from anorthite. This process leads to the dissolution of anorthite and the precipitation of kaolinite, while calcite transitions from a dissolved state to a precipitated state over time. The analysis indicates that structural trapping provides the highest storage contribution during the injection phase, whereas residual gas trapping becomes dominant by the end of the simulation. Notably, it is observed that the storage contribution of structural trapping decreases from 28.39% to 19.05%, and the percentage increase in storage contributions of residual gas, solubility, ionic, and mineral trapping are 4.12%, 3.25%, 1.69%, and 0.28% for CO2 plus water injection, thereby improving the long-term security of CO2 storage in offshore basins. It is most beneficial to optimize the layout and design of the injection well to ensure a uniform distribution of carbon dioxide and to increase the injection rate.

Precisely regulation of photochromism via absorption-induced photoreaction attenuation and light intensity dependence for time-resolved asymmetric encryption

With the growing need for secure information storage, encryption materials are required to be designed with high information capacity, easy to prepare, and hard to decode. Here, donor–acceptor Stenhouse adducts (DASAs), known for their straightforward synthesis, are used to develop a new time-resolved asymmetric encryption strategy, enhancing information capacity. The photoswitching property of DASAs is regulated by the intensity of initial absorbance and the intensity of light irritation. Besides, we recognize the relationship between the intensity of initial absorbance and photoswitching rate constant is caused by the absorption-induced photoreaction attenuation (AIPA). By rationally regulating the photo-bleching time of DASAs, the information can be sequentially revealed (colored state) or concealed (bleached state) in the desired temporal order. As a result, a quick response (QR) code label for practical application and a multi-level encoding scheme for sophisticated asymmetric encryption have been proposed. Specifically, the advanced asymmetric encryption technique employs layer sequence and light intensity as private keys, diverging from traditional public key systems. Information is sequentially displayed over time. The accurate information can only be revealed at the correct temporal dimension (layer sequence) and the appropriate decoding parameter (light intensity), both of which are precisely controlled by AIPA. This study not only unveils an approach to time-resolved asymmetric encryption using DASAs, enhancing the security and capacity of information storage, but also paves the way for the development of high-security encryption materials that are both highly efficient and resistant to unauthorized decoding efforts.