Assistant-Based Integrity Auditing Scheme With Privacy Protection Function for Cloud Storage

The increasing reliance of governments on cloud computing infrastructure located outside their territorial boundaries challenges traditional conceptions of sovereignty in international law. This article examines whether existing international legal frameworks adequately protect governmental data and services hosted abroad, prompted by incidents like the SolarWinds cyberattack that demonstrated the vulnerability of cloud-dependent governmental functions. Through systematic analysis of state positions on cyber sovereignty and the principle of non-intervention, this study reveals significant divergence in how states conceptualize sovereignty violations in cyberspace. While some states adopt purely territorial approaches requiring physical effects within their borders, others embrace functional interpretations that protect governmental operations regardless of the location of supporting infrastructure. The paper argues that traditional territorial sovereignty concepts prove inadequate for addressing modern cloud-based governance realities, creating dangerous protection gaps for digitally transformed states. Estonia’s data embassy in Luxembourg and Ukraine’s wartime cloud migration illustrate these challenges clearly. The analysis suggests that international law must evolve through progressive interpretation of existing principles—reconceptualizing sovereignty to protect a state’s capacity to govern within its territory and clarifying that non-intervention protects against coercion without territorial limitations—to address contemporary digital governance while preserving fundamental legal structures.

Cloud computing has emerged as a dominant paradigm for data storage and management, offering scalability, accessibility, and cost efficiency. However, the increasing reliance on cloud platforms raises significant concerns regarding data confidentiality, integrity, and resilience against evolving cryptographic attacks. This survey explores secure file storage in cloud environments through the integration of hybrid cryptography combined with algorithm rotation mechanisms. Hybrid cryptography leverages the strengths of symmetric algorithms for high-speed encryption and asymmetric algorithms for secure key exchange, thereby ensuring both efficiency and robustness. To further enhance security, algorithm rotation introduces dynamic switching between multiple encryption schemes, mitigating risks associated with algorithm obsolescence and cryptanalytic breakthroughs. The paper systematically reviews existing approaches to cloud data protection, evaluates the effectiveness of hybrid models, and highlights the role of algorithm rotation in sustaining long-term security. Comparative analysis of prior works demonstrates that combining hybrid cryptography with adaptive rotation significantly reduces vulnerabilities while maintaining performance scalability. This survey concludes by identifying open challenges, including computational overhead, interoperability, and key management complexities, and proposes future research directions toward resilient, adaptive, and standards-compliant secure cloud storage frameworks.

Cloud computing involves collecting data and software along with seamless access over the internet rather than the computer's hard drive. Cloud storage facilitates files and programs to be downloaded and uploaded, and viewed over the internet instead of a computer's hard disc, using any digital device. This modern production age transforms industries worldwide with an increasingly digital future where the cloud provides a special processing, storage and networking capability. This technology is an essential component of Industry 4.0; it recognises emerging innovations to investigate methods to be modified to fulfil current customer requirements. Due to significant advances in the Internet of Things (IoT), robotics, cloud-based technologies and Artificial Intelligence (AI), the fourth Technological revolution is now being realised. This new revolution is triggering tremendous transformations in different areas, particularly in financial services, products, healthcare, automobiles building services. This paper details cloud computing's significant potentials and a study on different cloud computing applications for Industry 4.0. Calculation services make it possible for the platform, leading in the long term to innovative technologies, combining automation, the Internet of Things, and robotics.

With the rapid advancement of Internet of Things (IoT) and Wireless Sensor Networks (WSNs), healthcare systems have evolved to support continuous patient monitoring, real-time data acquisition, and cloud-based decision support. The secure transmission of sensitive medical data and the reliability of healthcare decision-making remain major challenges. Traditional routing techniques fail to provide robust trust management, making the system vulnerable to malicious nodes and unreliable data paths. The lack of lightweight, end-to-end encryption increases the risk of data breaches during transmission. Compounding the issue is the limited diagnostic accuracy of conventional analytics platforms, which struggle to effectively process complex, high-dimensional healthcare data. To address this, this study introduces a Dual Secure optimal Trusted routing (DST-Route) technique designed to ensure secure, trust-aware data transfer and enhance patient diagnostic decision-making in IoT-WSN. In the data transfer phase, the Enhanced Pomarine Jaeger Optimization (EPJO) algorithm is used to perform trust-based clustering and optimal cluster head selection, ensuring that only reliable nodes participate in data transmission. The sensitive health data collected from patients is protected using SmartNetcryption, a lightweight encryption used to secure information before cloud storage. In the analytics phase, the framework uses pre-trained deep learning models, including ResNet, DenseNet, EfficientNet, and UNet for feature extraction, while a Modular Deep Transfer Learning (MDTL) enables accurate healthcare state prediction and early diagnosis. Experimental results demonstrate that DST-Route significantly improves trust accuracy, energy efficiency, and prediction performance when compared to conventional routing techniques. The proposed UNet, combined with the MDTL model, achieved a healthcare state prediction accuracy of 98% with a loss rate of 0.05, showing 12.54% improvement over state-of-the-art models. This performance underscores the effectiveness of the DST-Route technique in ensuring secure and reliable sensitive data transfer for accurate patient state prediction.

The conventional ECG methods used to monitor vital signs are limited by their reliance on hospital equipment, restricted accessibility, and the late onset of diagnosis. To overcome these obstacles, an IoT-based wearable healthcare device is suggested to track the real-time ECG and analytics in the cloud. The product combines wearable ECG, SpO2, and heart rate variability sensors with an IoT microcontroller, backed by optimized communication protocols and cloud storage. A hybrid CNN-LM Deep Learning Model, based on arrhythmia classification, is employed, and mathematical models are utilized to compare energy efficiency and latency. In experimental testing, an accuracy of 98.6%, a sensitivity of 97.9%, a specificity of 98.2%, a precision of 98.3%, a F1-score of 98.1%, an average latency of 45 ms, a packet delivery ratio of 99.2%, and an energy consumption of 18.7 mW were achieved. These findings support the efficiency of the developed system in providing scalable, energy-efficient, and accurate real-time cardiac monitoring to support innovative healthcare applications.

<p>The widespread adoption of cloud storage systems has increased the demand for cryptographic mechanisms that ensure data confidentiality while limiting security risks associated with static and long-lived encryption keys. Although hybrid RSA–AES schemes are commonly employed to balance security and computational efficiency, key management—particularly autonomous and quantitatively bounded key rotation—remains insufficiently formalized. This study proposes a hybrid RSA–AES cryptosystem equipped with an autonomous auto-key rotation mechanism defined through explicit analytical constraints. AES-256 is employed for bulk data encryption, while RSA-2048 is used for secure encapsulation of symmetric session keys. Key renewal is governed by inequality-based conditions on elapsed time (Δt ≤ 30 minutes) and encryption usage (n ≤ 10 operations), yielding a mathematically bounded key lifecycle without manual intervention or external infrastructure. System performance and operational security properties are evaluated in a simulated cloud environment using file sizes ranging from 100 KB to 10 MB. Quantitative metrics include encryption and decryption time complexity, computational overhead relative to AES-only encryption, key variability measured by Hamming distance, and data integrity verification using SHA-256. Experimental results demonstrate linear scalability and a stable average overhead of approximately 12.8%, indicating a bounded constant-factor cost independent of workload size. Successive AES-256 keys exhibit a mean Hamming distance of 127.42 bits, consistent with high key variability and effective key freshness. These findings show that analytically constrained key rotation enables controlled symmetric-key exposure while preserving practical efficiency overall.</p>

The security of cloud storage systems remains a critical challenge as the in-creasing interconnection of services exposes them to a wide range of cyber threats. This paper presents a methodology for analyzing the structural characteristics of vulnerabilities and threats in cloud environments using Q-analysis and associated metrics. By modeling the interdependencies between vulnerabilities and threats, the study provides a systematic framework to construct attack profiles and evaluate their likelihood of occurrence. The approach bypasses the direct construction of simplex complexes by employing incidence matrices to derive structural trees, local maps, and connectivity graphs, thereby simplifying the analysis process. Using real-world vulnerability statistics from the Edgescan report, we identify the most exploited weak-nesses, such as cross-site scripting and broken authentication, and link them to corresponding attack vectors. A statistical model of characteristic attack profiles is then developed by applying entropy-based optimization methods, particularly the Nelder-Mead algorithm, to estimate probabilities of threat realization under structural constraints. The findings demonstrate that this method enables more accurate classification and ranking of threats, offering a practical tool for risk assessment and decision-making in cybersecurity management. Ultimately, the proposed approach provides a foundation for improving resilience of cloud storage systems through informed protection strategies.

The rapid growth of mobile technology has significantly transformed the healthcare industry by enabling convenient and on- demand access to medical services through smartphones. Mobile health applications have reduced dependency on traditional hospital visits and improved accessibility to healthcare resources. This shift has enhanced patient engagement and streamlined healthcare service delivery by integrating essential medical functions into a single digital platform. This project focuses on the development of a comprehensive Android application designed to improve healthcare delivery by facilitating doctor appointment booking, medicine ordering, healthcare package exploration, and consultation with top doctors in the user’s locality. The application is developed using Java in Android Studio with a Firebase backend to support real-time data management, secure user authentication, and reliable cloud-based data storage. These technologies ensure scalability, fast data synchronization, and secure handling of sensitive medical information. The application also integrates a secure payment gateway to enable hassle-free transactions for medicine purchases, appointment fees, and other medical services. The authentication system ensures secure session management, while cloud storage and real-time databases manage critical functionalities such as storing prescriptions, tracking medicine orders, and updating doctor availability. Overall, the proposed healthcare application provides an all-in-one solution for managing health needs through smartphones, offering improved accessibility, enhanced user experience, and efficient connectivity between patients and healthcare providers. Furthermore, the system ensures data security and reliability through robust authentication and encrypted storage mechanisms. Its modular architecture supports future enhancements such as telemedicine and advanced health analytics. By leveraging cloud technologies and a user- centric design, the application enhances healthcare efficiency and service quality.

With the rapid development of information technology and the deepening of digitalization, operating systems are increasingly applied in critical information infrastructure, making data security issues particularly important. Traditional cloud storage auditing models based on third-party auditing authorities (TPA) face trust risks and potential data leakage during data integrity verification, which makes them inadequate to meet the dual requirements of high security and local controllability in the current information technology environment. To address this, this paper proposes a system-wide data security auditing method for the Chinese Linux operating system, constructing a lightweight and localized framework for sensitive information protection and auditing. By dynamically intercepting system calls and performing real-time content analysis, the method achieves accurate identification and visual masking of sensitive information, while generating corresponding audit logs. To overcome the efficiency bottleneck of traditional pattern matching in high-concurrency environments, this paper introduces a Chinese Aho-Corasick (AC) automaton-based character matching algorithm using a hash table to enhance the rapid retrieval capability of sensitive information. Experimental results demonstrate that the proposed method not only ensures controllable and auditable sensitive information but also maintains low system overhead and good adaptability, thereby providing a feasible technical path and implementation scheme for data security.

The work is devoted to the development of a mathematical model and a program for online modeling of the pulse jet engine working cycle and charactiristics to assess the possibility of its use on UAVs. The developed universal thermodynamic model describes the instantaneous change in pressure and temperature, taking into account the mixing of flows, combustion and heat exchange in the combustion chamber of valved and valveless types of pulse jet engines, as well as the flows through the pipes and reed valves of various designs. Unlike the known works, a special online program, Pulsejet-Sim, for pulsejet modeling by a wide range of users, has been developed for the first time, which is posted on a special website as a web-oriented software service that does not require downloading to the user's computer and allows for instant calculation using server resources and secure data cloud storage. Using the developed software, preliminary mathematical modeling of known samples of pulse jet engines was performed, which showed generally satisfactory qualitative and quantitative agreement of the modeling results (error less than 10%) with the available experimental data on thrust, specific fuel consumption, cycle frequency and other parameters.

Empirical research conducted over the past few years suggests that the rapid expansion of digital bibliographic data in cloud-based library management and data retrieval systems has heightened concerns regarding confidentiality and privacy. Classical methods of encryption protect stored data, but they do not permit efficient access to encrypted data. This lack of functionality makes the system nearly unusable. This study develops a homomorphic encryption technique for safe bibliographic cloud storage and retrieval in order to address these problems. In order to accomplish security and access control goals without sacrificing the effectiveness of cataloging and retrieval systems, partially homomorphic encryption is utilized. It is more secure than both encrypted and unencrypted systems. When forced to employ completely homomorphic techniques, libraries and other archive systems frequently experience significant computational delays, which makes the systems slow. For bibliographic databases with high query traffic, systems that use the suggested LHE framework scale with nominal computing cost, have low latency, and have minimal ciphertext overhead—the ideal setup for systems that need to respond quickly. The algorithm's effectiveness is confirmed by its ability to survive numerous known cryptographic assaults while exceeding traditional systems in terms of encryption speed, storage efficiency, and query response time. By strengthening secure cataloging, controlled access, and privacy-centered search operations, LHE enhances bibliographic control systems and is beneficial for academic cloud infrastructures and next-generation digital libraries. This research augments the domains of cloud security and library and information science by offering innovative strategies for the secure management of bibliographic data.

In modern IoT ecosystems, the ability to efficiently collect, store, and analyze real-time sensor data is essential for enabling timely and data-driven decision-making. Traditional cloud database solutions often involve complex configurations, recurring maintenance burdens, and high deployment costs, making them unsuitable for lightweight, scalable, or educational IoT applications. To address these limitations, this project presents Sensify, a fully automated cloud-based data acquisition and analytics framework built using Google Sheets and Google Apps Script. The system enables seamless sensor data ingestion through a custom API, which accepts JSON-based payloads and appends the readings to a dynamically managed spreadsheet acting as a cloud storage layer. Automated routines periodically convert accumulated data into CSV format, transmit the file to external endpoints via HTTP POST, and reset the sheet for uninterrupted operation, thereby eliminating manual intervention and reducing backend infrastructure requirements. To enhance the interpretability and decision-making capability of the stored data, AI-driven analytical modules are integrated to compute statistical summaries, detect anomalies, and identify emerging trends across environmental parameters such as temperature, humidity, light intensity, and air quality. These insights are visualized through correlation plots, time-series analyses, and distribution characteristics, enabling users to observe patterns, diagnose sensor issues, and monitor environmental behaviour effectively. The proposed system offers a low-cost, scalable, and highly accessible solution suitable for IoT monitoring, educational deployments, research applications, and lightweight cloud analytics. By combining real-time ingestion, automated processing, and intelligent analytics, the framework demonstrates a robust approach for building practical, maintenance-free IoT data pipelines that support rapid insights and reliable long-term operation.

This study aims to design and implement a web-based campus information system integrated with an automatic cloud-based database backup mechanism to improve data security and operational efficiency. The research adopts a system development approach through needs analysis, system design using UML, interface prototyping with Figma, and implementation using PHP, MySQL, and cloud storage integration. The backup mechanism is automated using scheduled processes to ensure consistent data protection without manual intervention. System evaluation was conducted through a questionnaire distributed to 50 respondents, selected using the Slovin formula, to measure user perceptions of efficiency, reliability, and system performance. The results indicate a high level of user acceptance, with an overall average score of 4.44, reflecting strong system reliability and effectiveness. The automatic backup integration significantly reduces the risk of data loss, enhances accessibility, and optimizes cloud storage utilization. The findings demonstrate that the developed system provides a robust solution for campus data management and supports sustainable digital transformation in higher education institutions.

Abstract Cloud storage services such as Microsoft OneDrive are widely used in modern computing environments, making them an important source of digital evidence during forensic investigations. However, the proprietary nature of OneDrive synchronization mechanisms and the absence of explicit user-action logs pose significant challenges for accurate forensic analysis. This study examines OneDrive synchronization artifacts generated on Windows operating systems by performing controlled user actions and analyzing the resulting client-side forensic artifacts. A structured experimental methodology was adopted to correlate local file system activities with OneDrive client logs to reconstruct synchronization behavior. The analysis demonstrates that OneDrive synchronization activity can be reliably reconstructed by identifying and correlating distinct synchronization cycles, even in the absence of explicit file-level logs. The findings highlight the forensic significance of OneDrive client logs and provide practical insights for investigators analyzing cloud synchronization activity on Windows systems. Keywords: Digital Forensics, OneDrive, Cloud Storage, Windows Operating System, Synchronization Artifacts

With the growing adoption of hybrid cloud storage, the identification and protection of sensitive information within large-scale unstructured data has become increasingly challenging. Traditional rule-based and machine learning approaches have limitations in context-aware sensitive data classification and large-scale processing. In this work, a novel framework named LLM-SPSS, implementing a secure and confidential storage layout for distributed hybrid clouds through a fine-tuned XLM-R Base model and multi-dimensional data partitioning, is proposed. First, a fine-tuned XLM-R Base model with adaptive prompt tuning is employed to enable context-aware sensitive data classification and improve detection accuracy. In addition, MapReduce-based distributed processing allows the framework to scale efficiently to large datasets, thus enhancing computational efficiency. Furthermore, a multi-dimensional cloud partitioning scheme provides secure and fine-grained storage isolation within hybrid cloud environments. Experimental results demonstrate that LLM-SPSS achieves an F1-score of 99.66% and yields a 6.3× speed-up over the non-distributed baseline, outperforming traditional rule-based (F1 68.27%), conventional machine learning (SVM F1 98.32%, Random Forest F1 95.79%), and other LLM-based approaches (DePrompt F1 95.95%) and effectively balancing high accuracy with computational efficiency.

Cloud storage requires efficient and secure encryption to ensure data confidentiality.This study evaluates and compares the performance of the AES and Blowfish encryption algorithms with the aim of determining which algorithm offers superior efficiency and reliability for secure data processing. The specific objectives are to measure and analyze their encryption time, execution time, throughput, and Mean Square Error (MSE) across multiple experimental runs. MATLAB was used as the primary methodology for implementing both algorithms, generating datasets, executing repeated trials, and computing performance metrics. Execution time values were recorded for twenty samples, where AES consistently produced lower times such as 72 s, 154 s, 95 s, 78 s, 25 s, and a minimum of 9.1 s, while Blowfish recorded higher corresponding values including 106 s, 213 s, 138 s, 136 s, 31 s, and a minimum of 10 s. Comparative averages further showed that AES achieved a lower overall execution range, indicating faster computational behaviour. Throughput values also demonstrated AES superiority, with sample values above 1.00, while Blowfish maintained lower throughput levels. MSE analysis revealed significantly lower values for AES, such as 59.88, compared to Blowfish’s much higher 126.83, indicating better data accuracy and reduced distortion during encryption and decryption. The bar and line graph analyses confirmed AES’s consistent performance advantage across all metrics. The results demonstrate that AES outperforms Blowfish in terms of speed, efficiency, and reliability. In conclusion, AES is better suited for high-performance encryption applications requiring fast execution and accurate data reconstruction. Blowfish, although functional, shows slower and more inconsistent behaviour, making it less ideal for time-critical or high-volume security systems.

This paper presents Jub, a Life Science and Healthcare Data Platform (LSHDP) based on generic sandboxes that integrate AI tools and cloud storage into big data science services. Jub automatically and transparently creates data science services to transform datasets into massive information products by using a profiling methodology. These products are presented by generic-secure cloud-based FAIR observatories adding Programmable, Configurable/Customizing, Adaptable, and Resiliency properties (PCA-FAIR-R). This enables organizations to conduct and customize complex analytics processes to support decision-making. We conducted a study case to convert mortality, climate, and pollutants datasets (2000-2023) reported by the Mexican Government into a solid core hub of information products: 16 strategic data observatories based on 85,171,404 information products created from 114,155,622 spatio-temporal profiles of the International Classification of Diseases (ICD-10) mortality classes/strata and cancerogenic substances. An exploratory study revealed highlights about the significance of breast cancer mortality rate growth showing possible associations with air pollutants. This paper also describes the lessons learned from the practice and experience of implementing Jub sandboxes-based observatories for the Population-based Cancer Registry Network deployed on the Mexican territory in 12 Mexican states by public healthcare institutions, as well as to implement bone cancer deep-learning-based diagnosis at a national Hospital.

Abstract - Travel applications today commonly focus on bookings or travel information, but they do not address the difficulties solo travelers face in finding reliable companions and staying safe. TravelMate aims to bridge this gap by connecting travelers with similar interests while supporting them with essential planning and safety tools. The application allows users to discover destinations, read travel blogs, explore location suggestions, manage favourites, and filter potential travel buddies based on chosen preferences. To enhance safety, TravelMate includes a secure login process using Firebase Authentication and provides an SOS emergency alert system that instantly shares the user’s location with trusted contacts. A lightweight chatbot named Olivia assists users by offering predefined travel guidance and app navigation help. The application is built using Flutter and Firebase services, ensuring smooth data synchronization and scalable cloud storage. Overall, TravelMate enables solo travelers to plan their journey confidently, stay informed, and connect socially in a safe environment—all within a single mobile application. Keywords: - Solo Travel, TravelMate, SOS Alert, Travel Buddy Matching, Chatbot Assistance, Firebase, Flutter

XTS is a block cipher mode for storage encryption. IEEE and NIST have standardized it, and it is widely deployed in real-world applications, including FileVault2, Bitlocker, and dm-crypt. However, it is well-known that XTS provides limited confidentiality and no integrity. XTS prevents simple attacks, e.g., information extraction from a stolen device. However, applications of XTS are expanding, such as cloud storage and CPU memory, where this issue implies a significant security threat. To address this issue, we propose iXTS, a family of black-box conversion methods of XTS into an authenticated encryption (AE). To make our proposal usable in practice, we need to assume that the only controllable part of the XTS engine is the plaintext input, because XTS engine’s ciphertext output is typically directly connected to the storage device and we assume the adversary is able to access the device directly, in addition to the black-box access to the engine. It is also desirable that the conversion could be done without the knowledge of the internal XTS key and without touching it. These constraints pose non-trivial technical challenges, and iXTS is the first effective solution meeting these constraints. We prove that each member of iXTS achieves n/2-bit security as a randomized AE using an n-bit block cipher. This security level is equivalent to popular AE modes such as GCM. iXTS is efficient as it requires no additional cryptographic computation beyond the original XTS. Plaintexts are expanded by a small amount, which is necessary for achieving AE. Our benchmarks on Intel platforms with AES-NI demonstrated that iXTS incurs only minor computation overhead from the underlying XTS.