IoT-Enabled Cloud Storage Data Access Control ModelBased on Blockchain Technology

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

BackgroundMost research scientists working in the fields of molecular epidemiology, population and evolutionary genetics are confronted with the management of large volumes of data. Moreover, the data used in studies of infectious diseases are complex and usually derive from different institutions such as hospitals or laboratories. Since no public database scheme incorporating clinical and epidemiological information about patients and molecular information about pathogens is currently available, we have developed an information system, composed by a main database and a web-based interface, which integrates both types of data and satisfies requirements of good organization, simple accessibility, data security and multi-user support.ResultsFrom the moment a patient arrives to a hospital or health centre until the processing and analysis of molecular sequences obtained from infectious pathogens in the laboratory, lots of information is collected from different sources. We have divided the most relevant data into 12 conceptual modules around which we have organized the database schema. Our schema is very complete and it covers many aspects of sample sources, samples, laboratory processes, molecular sequences, phylogenetics results, clinical tests and results, clinical information, treatments, pathogens, transmissions, outbreaks and bibliographic information. Communication between end-users and the selected Relational Database Management System (RDMS) is carried out by default through a command-line window or through a user-friendly, web-based interface which provides access and management tools for the data.ConclusionepiPATH is an information system for managing clinical and molecular information from infectious diseases. It facilitates daily work related to infectious pathogens and sequences obtained from them. This software is intended for local installation in order to safeguard private data and provides advanced SQL-users the flexibility to adapt it to their needs.The database schema, tool scripts and web-based interface are free software but data stored in our database server are not publicly available. epiPATH is distributed under the terms of GNU General Public License. More details about epiPATH can be found at .

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

The exponential growth of data storage and sharing in cloud demands an efficient access control mechanism for flexible data sharing. Attribute-Based Encryption (ABE) is a promising cryptographic solution to share data among users in the cloud. But it suffers from user revocation, attribute revocation, forward secrecy and backward secrecy issues. Communication and computation overhead is more due to the linear variation in the size of ciphertext and the secret key with respect to the number of attributes. In this paper, we investigate an on-demand access control for flexible sharing of secure data among randomly selected users. It is a tunable access control mechanism for the flexible sharing of ciphertext classes in the cloud. It delegates the decryption rights of any set of ciphertext classes among the users only if their attributes are satisfied with the access policy associated with ciphertext and if they should possess a compact key corresponding to the intended set of ciphertext classes. It produces a constant size ciphertext and a compact secret key to efficiently utilize the storage space and reduce the communication cost. The compact key aggregates the power of secret keys used to encrypt the outsourced data. This method flexibly shares the ciphertext classes among the randomly selected users with a specific set of attributes. All other ciphertext classes outside the set remain confidential. It allows dynamic data updates by verifying the data manipulation privilege of users with the help of claim policy. The proposed scheme provides access control of varying granularity, at user-level, at file-level, and attribute-level. Granularity levels can be chosen based on applications and user demands. Hence, it is a multi-level, tunable access control over the shared data. It is very useful for secure data storage. This scheme tackles user revocation and attribute revocation problems so that, it allows the data owner to revoke a specific user or a group of users. It prevents forward and backward secrecy issues.

This study examines the conceptual models for optimizing infrastructure solutions for ISPs based on cloud technologies. The relevance of this research is justified by the rapid technological advancements that serve as the foundation for infrastructure solutions in internet service providers (ISPs). Their optimization requires a systematic approach that considers load balancing, distributed data storage, security issues, and regulatory aspects. However, there are contradictions in the scientific literature regarding optimization methods. The goal of this article is to systematize the understanding of conceptual models for optimizing ISP solutions, taking into account modern cloud technologies and their evolution. The conducted analysis identified key research directions and existing gaps in studying the interrelationship between technical, economic, and legal factors. As a result, an author's perspective was formulated on the prospects of integrating cloud solutions into ISP infrastructure, considering scalability efficiency, fault tolerance, and information security. This includes deep integration of analytical tools, synergy with quantum computing technologies, and standardization unification. The presented materials will be useful for researchers in the field of digitalization, network technology specialists, internet service providers, and developers of relevant platforms.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

In order to protect the security and privacy of big data, the cloud storage service needs to enforce effective access control mechanism on user requests. Attribute-Based Encryption is a promising cryptographic access control technique to ensure the end-to-end security of data in cloud. However, the existing ABE researches mainly focus on the efficiency decryption, while the flexibility of policy, the communication cost, and the metadata management of ciphertexts are still challenging issues in the big data environment. In this paper, for the first time, we propose a new distributed, scalable and fine-grained access control scheme based on classification attributes for the cloud object storage. The classification attributes and threshold policies are integrated into an access structure, and then the objects are encrypted with the integrated access structure. The constant-size ciphertext components related to attributes can be managed as the corresponding metadata. As a result the encryption complexity and ciphertext storage are reduced. In addition, we present a new label-based access control model with multi-authorities to describe the detailed relationships of entities in our scheme. Besides, the proposed scheme is proved to be secure under l-BDHE assumption, and the system implementation demonstrates the practical feasibility and good performance.

The ease of online searching has diminished people's expectations of privacy and the ability to control access to information about oneself, which can alter basic assumptions about the therapy relationship. This grounded theory study explored psychotherapists' experiences of searching online for patients and being searched for by patients, among 28 clinicians of primarily psychodynamic orientation. Many therapists search online for information about patients, but often minimize or rationalize this action and do not share it with their patients. Meanwhile, while they believe their patients search for them, they find it difficult to raise the topic directly in therapy. Thus, when it comes to online searching and discovery of information, an air of secrecy pervades the therapeutic relationship. Despite the serious implications that the pervasiveness of online searching has for the treatment relationship, therapists lack sufficient support for exploring their motivations, curiosities, and urges surrounding this action.

In this paper, a variety of cloud service combinations is used to form the control core of a smart home, realize data forwarding, storage, and analysis in the cloud, and complete the remote management of smart home devices. This paper studies the secure access control of smart home data combined with the blockchain technology and password technology to realize the secure and efficient access control of smart home data. The system can achieve the goal of independent research and development. Aiming at the problem that the access control of a smart home is generally managed by third‐party authorized institutions and there is an unauthorized access, a blockchain‐based smart home (BSH) access control scheme is proposed. The scheme extends the attribute‐based access control model (ABAC) and applies the blockchain technology to the smart home ABAC model to realize fine‐grained access control. In the scheme, the resource provider first publishes the access control policy of the resource to the blockchain. If the resource visitor wants to access the resource, he needs to submit an access request to the blockchain and use an SM2 threshold signature to process the transaction proposal. The endorsement peer node in the blockchain runs the access control policy smart contract to decide whether to grant access rights. This scheme can ensure that resource providers can participate in all the processes of access control and avoid the risk of unauthorized access caused by the centralized management of the third party. Finally, the simulation experiment is carried out on the Hyperledger Fabric alliance chain development platform. The results show that the BSH access control scheme has good applicability in the smart home scenario.

With the rapid development of intelligent medical digitization and people's aspirations for a healthy life, the issue of medical data security has become increasingly prominent. The sensitivity and value of medical data demand that we take effective measures to safeguard data security. This not only pertains to the risks of data leakage and personal information abuse but also to issues of social stability. To address the problem of medical data security, the advantages and characteristics of the distributed architecture of fog computing are primarily utilized, and RSA algorithm technology is integrated. This enables the effective processing and storage of the large volume of heterogeneous data generated by medical service systems in the context of the Internet of Things at fog nodes, achieving low-latency processing and efficient storage of medical data and ensuring the data security of data sources. Simultaneously, technical means such as identity authentication and access control, fine-grained permission management, and data encryption in intelligent medical service systems are employed to enhance data security protection. Research has demonstrated that the integration of fog computing and RSA algorithm technology can effectively reduce computational costs, ensure the security of medical data flow, protect patient privacy, and prevent improper information access and abuse. The intelligent medical data security technology based on fog computing is of great significance for protecting patient privacy and data security. Meanwhile, it plays an important role in safeguarding medical data, improving service quality, and promoting industry development, and it represents an important research direction in the future intelligent medical field.

Aiming at the security issues about isolation storage, access, and sharing of the multi-tenant data under cloud environment, based on the Hadoop storage architecture, this paper proposes a Tag-Based protection method for Multi-tenant data security. For the isolation storage and access security of the private data, by introducing security tag to participate in storage and authentication access of the data, a consistent hash improved algorithm on dual authentication of dynamic password and security tag is proposed, realizing efficient isolation storage and secure access of the multi-tenant private data. For the security of the sharing data, a tag-based proxy re-encryption data sharing scheme is proposed, completing data secret sharing under the security authentication of the tenant sharing tag. For proxy re-encryption, use sharing tag to replace sharing data. Through the analysis of the security and instantiation of the method, this method is proved to be reliable and feasible under the cloud environment.

This paper presents a novel method and system for flexible data storage management that incorporates dynamic encryption key techniques to ensure data security.The proposed approach determines storage operations and selects optimal storage resources based on predefined criteria such as performance, cost, and data access patterns.The integration of cryptographic keys ensures the confidentiality and integrity of data, addressing security concerns in diverse and large-scale data storage environments.By leveraging adaptive key management, the system can respond to changing security threats and operational requirements in real-time.This adaptability enhances the resilience of data storage solutions against emerging cyber threats and operational disruptions.The system leverages advanced algorithms to study data access patterns, optimizing storage operations for both rapid performance and efficiency.Additionally, the proposed method features a robust mechanism for key rotation and revocation, which reduces the risk of key compromise and bolsters overall security.The dynamic selection of storage resources considers multiple factors, ensuring a balanced approach that meets both performance and cost-efficiency criteria.This comprehensive strategy not only enhances security but also boosts the overall performance of data storage systems.Extensive testing in diverse environments has shown significant improvements in data retrieval times, reduced storage costs, and faster encryption and decryption processes.The results demonstrate the practical applicability of the proposed system in real-world scenarios, showcasing its potential to revolutionize data storage management.The proposed system also integrates seamlessly with existing infrastructure, making it a versatile solution for a wide range of applications.By providing a detailed analysis of key performance indicators, this paper offers valuable insights into the benefits of integrating adaptive cryptographic mechanisms in data storage management.The innovative combination of dynamic operations and adaptive security measures positions this system as a leading-edge solution in the field.Future research will focus on further enhancing these adaptive mechanisms and exploring their application in more diverse storage environments.Additionally, the integration of machine learning algorithms to predict and adapt to data access patterns will be investigated to further optimize system performance.The findings of this paper contribute to the ongoing discourse on secure and efficient data storage, offering a novel perspective that bridges the gap between performance optimization and robust security measures.

Emergence of new technologies and modernization of existing information technologies, development of information and telecommunication processing and storage systems have increase the level of information security, necessitating an increase of information security’s effectiveness with the complexity of data storage architecture. Security of information from unauthorized access is an essential measure to prevent material and non-material damage to its owner. So it is very important to take into account the efficiency of the subsystem of access control and data security in order to ensure security of some information system. Accordingly the threat of information leakage has made the means of information security and cyber security one of the mandatory characteristics of information and telecommunication systems and information security has become an integral part of professional function. Under conditions of the hybrid war of the Russian Federation against Ukraine? the number of cyber attacks on military information and telecommunication systems has increased. At the same time their technological complexity has increased too. This process necessitates the improvement of information security systems and the process of providing access to them by using modern types of users identification. Access control is an effective method of information security. It regulates the use of information system resources. User’s identification is an important and integral element of access control system. An analysis of modern types of users identification is presented in the paper. The technology of QR-code is considered: the principle of formation, the types of coding, the structure of elements, the advantages of its usage. The interconnection between its components was established. Prospects for further research are the development of an algorithm and software application for user identification to provide access by QR-code to information and telecommunication systems for military purposes.

Chapter 4 - Security and privacy facets of electronic health record

Contraposing to the security of cloud storage service network and data sharing characteristics, a ciphertext access control mechanism based on CP-ABE algorithm is proposed. The access control mechanism is studied from two aspects: access control and access control architecture. The corresponding data structure of security algorithm is provided, and its simulation and performance analysis are performed. The security mechanism ensures the security of data in cloud storage system under open environment and reduces the complexity of privilege management through attribute management, under the premise that service providers are not credible. The cloud storage system designed and implemented in this paper achieves the original design object and performs well in all aspects, which is of great importance to the safe and effective storage of user's information and data in the era of large data.