Data Resilience: Secure Emergency Backup on Cloud

In today’s digital landscape, where data is a critical asset for businesses, building a resilient backup infrastructure is paramount to ensuring data availability and protection. This research investigates the synergy between three essential elements—data redundancy, encryption, and Role-Based Access Control (RBAC) to enhance backup system resilience. Data redundancy, achieved through methods like the 3-2-1 rule and cloud replication, ensures continuous availability and protection against hardware failures. Encryption secures backup data, making it unreadable to unauthorized users and safeguarding sensitive information in compliance with regulatory standards. RBAC, on the other hand, provides granular control over user access, enforcing the principle of least privilege and minimizing the risk of unauthorized data manipulation. By combining these strategies, organizations can mitigate the risks of data loss, breaches, and unauthorized access, creating a robust, secure, and efficient backup infrastructure. This paper provides a comprehensive analysis of how these technologies can be integrated, offers best practices for implementation, and discusses the future of backup systems in an increasingly complex threat environment. Keywords: Backup Infrastructure, Data Redundancy, Data Encryption, Role-Based Access Control, RBAC, Data Security, Disaster Recovery, Data Protection, Cloud Backup, Data Availability, Security Best Practices, IT Resilience, Cybersecurity, Backup Strategies.

The rapid adoption of cloud computing has transformed the way data is stored, shared, and accessed across organizationsand individuals. Despite its scalability and cost efficiency, cloud storage introduces serious security and privacy challenges, particularly when sensitive data is outsourced to third-party cloud service providers. Traditional access control mechanisms often rely on the cloud provider’s trustworthiness, which may expose data to unauthorized access, insider threats, and data breaches. To address these concerns, CryptCloud+ is proposed as a secure and expressive data access control framework that enforces fine-grained authorization directly through cryptographic techniques. CryptCloud+ focuses on ensuring data confidentiality while enabling flexible and policy-driven access control over encrypted cloud data. The system leverages encryption-based access control, where data is encrypted before being uploaded to the cloud, and only authorized users possessing valid cryptographic credentials can decrypt and access the data. This approach eliminates the need to trust the cloud service provider, as all sensitive operations related to data access are handled at the client side. A key contribution of CryptCloud+ is its support for expressive access policies, allowing data owners to define complex rules based on user attributes such as roles, departments, access levels, or time constraints. These policies enable fine grained access control without requiring data owners to manage individual permissions manually. The framework ensures that users can access only the data they are authorized for, while unauthorized users, including cloud administrators, are unable to infer any meaningful information from the encrypted data. The proposed system is implemented using Python, taking advantage of its robust cryptographic libraries, simplicity, and integration capabilities. Python-based modules handle key generation, encryption, decryption, and policy enforcement, making the system modular and easy to extend. The implementation demonstrates how secure key management and attribute-based access enforcement can be achieved efficiently in a real-world cloud environment. CryptCloud+ also addresses scalability and performance concerns by minimizing computation overhead during data access. Encryption and decryption operations are optimized to ensure that system performance remains practical even as the number of users and data files increases. Experimental results indicate that the framework maintains strong security guarantees while providing acceptable response times for authorized users. In addition to security, the system enhances usability by allowing seamless data sharing among multiple users without re encrypting data for each individual. This significantly reduces management complexity for data owners while preserving strict access control policies. The system design ensures compatibility with existing cloud storage services, making itadaptable to various deployment scenarios. Overall, CryptCloud+ presents a secure, flexible, and effiient solution for cloud data access control. By combining cryptographic enforcement with expressive policy definition and Python-based implementation, the frameworkstrengthens data security, protects user privacy, and improves trust in cloud storage systems. The proposed model demonstrates the feasibility of practical, cryptography-driven access control for modern cloud environments.

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

The rapid digitalization of healthcare systems has led to an unprecedented accumulation of sensitive patient data across various platforms, exposing the industry to growing risks of data breaches, unauthorized access, and integrity compromise. Ensuring the security, privacy, and trustworthiness of healthcare data is paramount to maintaining patient confidentiality and enhancing clinical decision-making. This study proposes a conceptual framework that synergistically integrates Blockchain technology and Artificial Intelligence (AI) to enhance healthcare data security. The framework is designed to address key challenges such as data integrity, access control, real-time threat detection, and secure interoperability across healthcare stakeholders. Blockchain, with its decentralized and immutable ledger capabilities, provides a robust foundation for tamper-proof data storage and transparent audit trails. Smart contracts are employed to automate access controls and ensure compliance with regulatory requirements. AI, on the other hand, plays a critical role in intelligent threat detection and anomaly monitoring. By leveraging machine learning algorithms, the framework can identify suspicious patterns, detect insider threats, and predict potential breaches in real time. The proposed architecture comprises four core components: Secure data ingestion, blockchain-based data storage, AI-powered analytics, and a privacy-preserving access management layer. The framework also incorporates role-based authentication and homomorphic encryption techniques to enhance data privacy while supporting authorized data sharing. Case scenarios such as electronic health record (EHR) exchange and remote patient monitoring are used to demonstrate the practicality and scalability of the model. This integrated approach not only ensures the confidentiality, integrity, and availability of healthcare data but also fosters trust among patients, providers, and regulatory bodies. The framework aligns with global healthcare data standards and complies with regulations such as HIPAA and GDPR. Future directions include the deployment of federated learning models to further decentralize AI training while maintaining data privacy across institutions.

In scenarios like smart healthcare, smart communi ties, and smart buildings, data collected by Internet of Things IoT devices often pertains to user privacy. However, due to the limited computing power and storage capacity of IoT devices, the data of Data Subject DS are generally stored in the cloud, causing DS to lose control over his data and increasing the risk of privacy leakage. Additionally, resource-constrained IoT devices often face affordability issues regarding encryption costs. In this paper, we propose PPFID, an efficient privacy preserving framework with the DS’s intentions. Specifically, PPFID enforces isolated compu tation and permission control via secure enclaves of Intel SGX on cen trally aggregated data, and encrypts data to guarantee confidential ac cess, computation, and delivery throughout the entire life of the data. To support fine grained access control with the wishes of DS as its core, we design the Privacy Metadata-Based Access Control PMBAC model, which consider the wishes of DS to make access control decisions for each piece of data. Compared to other schemes, PPFID provides more data processing methods and introduces access control schemes that are both strongly isolated and respect DS’s rights. We successfully implemented PPFID on Intel SGX and the embedded device, and evaluated the its feasibility. Our evaluation shows PMBAC can process an access request in the enclave in just 140 ms, meeting DS’s real-time requirements. Al though the computing time has increased compared to the non-protected environments, the prediction accuracy of VGG19 and CNN remains es sentially the same. Experimental results demonstrate that PPFID is ap plicable in general IoT scenarios involving users’ privacy data, and can ensure the confidentiality, integrity, and availability of data while re specting the wishes of DS

This paper introduces combined data integrity and availability attacks to expand the attack scenarios against power system state estimation. The goal of the adversary, who uses the combined attack, is to perturb the state estimates while remaining hidden from the observer. We propose security metrics that quantify vulnerability of power grids to combined data attacks under single and multi-path routing communication models. In order to evaluate the proposed security metrics, we formulate them as mixed integer linear programming (MILP) problems. The relation between the security metrics of combined data attacks and pure data integrity attacks is analyzed, based on which we show that, when data availability and data integrity attacks have the same cost, the two metrics coincide. When data availability attacks have a lower cost than data integrity attacks, we show that a combined data attack could be executed with less attack resources compared to pure data integrity attacks. Furthermore, it is shown that combined data attacks would bypass integrity-focused mitigation schemes. These conclusions are supported by the results obtained on a power system model with and without a communication model with single or multi-path routing.

In the article the problem of the system of urban management in emergency and crisis situations were considered. Composition and functions of the municipal government have been characterized. It was disclosed the concept of "emergency" and "crisis". Typology of crisis and emergency situations include: natural disasters, manmade disasters, sanitary-epidemiological, socio-political and economic crises. Crises have been classified: the reasons of occurrence, in the sphere of manifestation, on the possibilities of forecasting and duration. Typical measures to manage the liquidation of emergency situations in the city have been identified. Principles and steps of the City of crisis management are presented. Analysis was performed operational management experience life support in an emergency situation caused by the fighting. Was justified by the implementation of additional features for managing urban emergency response and overcome the consequences of the social, economic and environmental crisis. Perform additional functions involves restoration of vital facilities of the city, which are damaged by the fighting. Additional features of urban governance provide fault detection of communal, social, engineering, road infrastructure. Function provides an assessment permanently damaged facilities. Function ensures the development of the investment project for restoration of urban infrastructure. Analysis functions provide resource support the restoration project. Functions provide monitoring restoration projects of urban infrastructure.

In today’s digital era, safeguarding critical data is paramount, particularly within enterprise environments. Oracle databases are widely used for managing mission-critical data, making it essential to implement robust security measures to protect against data loss, corruption, and unauthorized access. This paper focuses on optimizing Oracle database security through automated backup and recovery solutions. The traditional manual processes for database backup and recovery are prone to errors, time delays, and human intervention, which may lead to security vulnerabilities. To mitigate these risks, this study explores the integration of automation technologies in the backup and recovery processes of Oracle databases, ensuring higher efficiency, consistency, and reliability. Automated solutions leverage scheduling, encryption, and real-time monitoring to provide a secure backup environment, minimizing the chances of data breaches. Moreover, they enable rapid recovery in the event of a disaster, thereby enhancing database availability and reducing downtime. The research discusses best practices for configuring Oracle database backups, including full, incremental, and differential backups, and highlights the role of encryption and access controls in securing backup data. Additionally, the study examines the use of cloud-based backup systems, which offer scalability and off-site storage, further enhancing security and disaster recovery capabilities. By adopting these automated solutions, organizations can optimize the security of their Oracle databases, ensuring data integrity, confidentiality, and availability. The paper concludes by recommending strategies for integrating automated backup and recovery solutions into existing Oracle database environments, aiming to streamline operations and mitigate security risks in the long term

Sanitary and epidemiological monitoring in Ukraine during full-scale Russian invasion 2022: place of military medical servicesIntroduction.The epidemic process peculiarities in emergency situations and during wars have significant differences and largely depend on the military action theater, combat actions engagement, season, climate, communal and household as well as sanitary-hygienic living conditions of population, the kind of weapon used, troops logistic support, application of biological weapons, and many other conditions.Purpose.The paper aimed to delineate key features of diagnostics of especially dangerous diseases and emerging infection diseases as well as expediting of the sanitary and epidemiological process of their detection in order to prevent them in the field in wartime.Materials and methods.The study used bibliographic method, as well as method of information synthesis.The analysis of data provided in this work helped to outline the main features of sanitary and epidemiological intelligence in detection of infectious process and its performance conception, which was conducted using the facilities of State Sanitary and Epidemiological Service of the Armed Forces of Ukraine.All data is relevant and can be interpreted as general guide to sanitary and epidemiological intelligence trials and is used to determine main routes of future service institutional evolution of sanitary and epidemiological service of the Armed Forces of Ukraine as effective and necessary establishment in the field of military medicine as a part of Ukrainian healthcare system in general. Results and discussion. Catastrophes and natural disasters in the absence of preventive sanitary-hygienic and antiepidemic measures, and corrective actions contribute to the spread of infectious diseases. Regardless of whether natural or industrial disasters occur, increase of morbidity among the population (personnel) and rescuers on acute intestinal and respiratory infections is expected, both natural focal and especially dangerous diseases, including through the untimely burial of the bodies of the deceased (tuberculosis, intestinal infections, hepatitis, HIV infection, anthrax).During an emergency situation in a separate territory or military conflict zone (during different time periods), infectious diseases, what are common to many armed conflicts, are being intensified: acute respiratory viral, Streptococcus and Staphylococcus, acute enteric infections, scabies, and those, which are spread among civilians and pathogens that footprint in the environment of the region during peaceful time.There wasn't any event of outbreak of an infectious disease among military personnel in Ukraine during 6 months of full-scale Russian invasion due to efforts of

Chapter 10 - MCSA/MCSE 70-290: Planning and Implementing Disaster Recovery

As COVID-19 continues to spread around the world, the healthcare industry has accelerated the transformation to digital healthcare services. In the era of big data, many hospitals prefer to use remote cloud servers to store and manage massive electronic medical data. However, cloud-assisted medical data systems cannot guarantee the confidentiality, integrity, and availability of data. Searchable encryption can effectively address the above challenges by enabling data search on the ciphertext, which achieves the availability of medical data while ensuring data security and privacy. However, the search server may return mismatched search results due to economic interests or single points of failure. Blockchain is a decentralized computing paradigm with public verifiability, which provides an efficient solution to this problem. However, the existing blockchain-based searchable encryption solutions do not consider the flexible search function of multiple users and the restriction of encrypted data for medical scenarios. Therefore, we propose a blockchain-based multiuser normalized searchable encryption (BNSE) scheme and design a blockchain-based normalized searchable encryption system for medical data (BNSEM) based on the scheme. To verify the practicality of the system, we evaluate the performance from both theoretical and experimental aspects.

Staff compliance is a crucial factor in maintaining the confidentiality, integrity, and availability of patient data, in line with the Minister of Health Regulation No. 269/2008 and the ITE Law No. 11/2008. EMR also increases the risk of sensitive patient data leakage if not managed with strict security procedures. Annisa Hospital Bogor, as an institution that has implemented EMR, is obliged to ensure the confidentiality, integrity, and availability of data. This study aims to measure and analyze the level of compliance of medical and non-medical staff at Annisa Hospital Bogor with information security procedures in managing Electronic Medical Records (EMDR). The method used is descriptive quantitative by distributing questionnaires to the EMR user staff population. The results showed that the average compliance rate was 81.6%, but vulnerabilities were found in the use of passwords and log-out protocols after accessing the system. The study recommends the need for regular training and systematic audits to improve staff awareness and discipline.

The study presented in the paper is concerned with a formal (theoretical and legal) model of regulations dedicated to the normalisation of employment relationships in emergency situations (states). By an ‘emergency situation’ the authoress means ‘overall exceptional circumstances caused by special, rare events, unforeseen in an accepted time perspective, in which someone or something was (is) at a certain time’. Until the outbreak of Covid-19, the subject matter of legal regulation of employment relationships in connection with the occurrence of emergency situations has not been given much thought (in our legal circle). This is also relevant to emergencies other than epidemics (warfare, natural disasters, force majeure, etc.). Insofar as research has been conducted to date, it rather focused on substantive and legal aspects of the regulations implemented in connection with the occurrence of such events, while any broader reflection on their formal and legal model has not been conducted. For this reason, in this paper, the authoress has firstly advanced a thesis that a distinction needs to be made between substantive and formal and legal models for the regulation of employment relationships in emergency situations, and secondly an attempt has been made to answer the question of which formal models can be distinguished in this context, what criteria should be used in this endeavour and how, on this basis, the distinguished models should be assessed. The authoress puts forward a thesis that formal models of ‘emergency’ labour law are closely linked to the concern to uphold the elementary democratic standards which should be respected by the law regardless of whether it relates to ‘ordinary’ or extraordinary situations. The research is primarily based on an analytical and linguistic, analytical and logical, dogmatic and legal, and descriptive method. It is conducted mainly on the example of the regulations of the Polish law and the decision of the Polish legislator taken in connection with the Covid-19 outbreak in relation to the method for the regulation of employment relationships during the period covered by it. However, the context of international (convention) regulations is also referred to. The authoress proposes to distinguish three basic formal models for the regulation of employment relationships in emergency situations: the permanent model, the mixed model and the ad hoc model. She concludes that, taking into account both the need for a relative flexibility in legal regulation and the need to maintain the elementary standards of a democratic state of law (primarily a guarantee that the applied legal solutions can be subjected to social discussion and dialogue between social partners), the mixed model should be considered as the most optimal. Keywords: Formal model of ‘emergency’ law; Emergency state; Emergency situation; ‘Emergency’ labour law; Employer’s risk

As the demand for data sharing and complex access-control policies continues to grow, traditional encryption mechanisms, which are generally established using a Public Key Infrastructure, face the problem of massive processing overheads and huge network bandwidth consumption. Attribute-Based Encryption (ABE) schemes have been proposed as a potential means of addressing these issues and have attracted great attention in recent years. Most previous studies on ABE focus on issues such as the revocation mechanism, multi-authority, the access structure design, and traceability. However, very few studies consider the storage efficiency problem and the present study proposes a novel data deduplication scheme based on ciphertext-policy ABE with convergent encryption and block-level data. The scheme can be deployed in third-party semi-trusted environments, and not only provides flexible, fine-grained access control over encrypted data, but also allows for the in-line elimination of redundancies in order to save cloud storage space. The experimental results show that the proposed scheme has an acceptable computational overhead and provides a promising solution for real-world data cloud storage and access scenarios.

High-end computing is suffering a data deluge from experiments, simulations, and apparatus that creates overwhelming application dataset sizes. This has led to the proliferation of high-end mass storage systems, storage area clusters, and data centers. These storage facilities offer a large range of choices in terms of capacity and access rate, as well as strong data availability and consistency support. However, for most end-users, the “last mile” in their analysis pipeline often requires data processing and visualization at local computers, typically local desktop workstations. End-user workstations---despite having more processing power than ever before---are ill-equipped to cope with such data demands due to insufficient secondary storage space and I/O rates. Meanwhile, a large portion of desktop storage is unused.We propose the FreeLoader framework, which aggregates unused desktop storage space and I/O bandwidth into a shared cache/scratch space, for hosting large, immutable datasets and exploiting data access locality. This article presents the FreeLoader architecture, component design, and performance results based on our proof-of-concept prototype. Its architecture comprises contributing benefactor nodes, steered by a management layer, providing services such as data integrity, high performance, load balancing, and impact control. Our experiments show that FreeLoader is an appealing low-cost solution to storing massive datasets by delivering higher data access rates than traditional storage facilities, namely, local or remote shared file systems, storage systems, and Internet data repositories. In particular, we present novel data striping techniques that allow FreeLoader to efficiently aggregate a workstation's network communication bandwidth and local I/O bandwidth. In addition, the performance impact on the native workload of donor machines is small and can be effectively controlled. Further, we show that security features such as data encryptions and integrity checks can be easily added as filters for interested clients. Finally, we demonstrate how legacy applications can use the FreeLoader API to store and retrieve datasets.