Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computational power. Cloud computing refers to data centers accessible to numerous customers over the Internet. It has the ability to utilize computing resources at a low cost and fast speed. Cloud data security is becoming increasingly important as we shift our devices, data centers, business processes, and other assets to the cloud. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption, which is the primary component of data security and the mechanism by which data or message transmission confidentiality and integrity are achieved. Current researchers have focused on genetics-based cryptography; genetics has been implemented in cryptography for data computation, storage, and transmission. This survey paper analyzes the new type of cryptographic approaches based on genetic science to generate robust sub-keys and robust cryptographic techniques based on homologous genetics (DNA, RNA, and mRNA) to improve security in the cloud. This work reviews several approaches based on genetic cryptography and their applications and drawbacks. In addition, the paper introduces the architecture of cloud computing along with the fundamental challenges of cloud computing applications.

ABSTRACT The healthcare industry, particularly with the advent of the Internet of Medical Things (IoMT), has witnessed significant integration of Internet of Things (IoT) technologies. IoMT is transforming healthcare by providing substantial benefits to both consumers and healthcare providers. However, the exponential growth in IoMT devices and their data generation raises critical challenges related to data analysis, security, and privacy. Traditional centralized artificial intelligence (AI) approaches, reliant on deep learning (DL) and machine learning (ML) algorithms, struggle to address the increasing complexity of sensitive medical data due to scalability and privacy concerns. Federated Learning (FL) emerges as a promising solution, enabling collaborative model training directly on IoMT devices while preserving data privacy by transmitting only model updates to central servers. This approach ensures data confidentiality and addresses privacy concerns associated with centralized systems. Despite its potential, research on FL in the context of IoMT remains limited. This paper examines the latest developments and innovations in FL, focusing on its application in IoMT and smart healthcare systems. It explores FL architectures, aggregation algorithms, frameworks, and their integration into IoMT‐driven healthcare applications. Additionally, the paper highlights challenges, including data heterogeneity, communication overhead, and security vulnerabilities, alongside privacy‐preserving techniques such as differential privacy, homomorphic encryption (HE), and secure multiparty computation (SMC). Finally, it identifies future research directions to advance FL‐powered IoMT solutions, offering valuable insights for academia and industry stakeholders aiming to enhance privacy‐preserving, intelligent healthcare systems.

Summary Cloud computing (CC) refers to the on‐demand availability of network resources, particularly data storage and processing power, without requiring special or direct administration by users. CC, which just made its debut as a collection of public and private data centers, provides clients with a unified platform throughout the Internet. Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Due to the exponential rise of cloud computing, systems based on cloud computing now require an effective data security mechanism. Comprehensive security policies, corporate security culture, and cloud security solutions are used to ensure the level of cloud data security. Many techniques exist to protect data communication in the cloud environment, including encryption. Encryption algorithms play an important role in information security systems and various cloud computing‐based systems. Current researchers have focused on lightweight cryptography, genetics‐based cryptography, and machine learning (ML) algorithms for security in CC. This review study analyses CC security threats, problems, and solutions that use one or more algorithms. The work discusses several lightweight cryptographies, genetics‐based cryptography and different ML algorithms that are used to overcome cloud security issues, including supervised, unsupervised, semi‐supervised, and reinforcement learning. Moreover, we enlist future research directions to secure CC models.

mHealth is the use of mobile devices and communication technologies for healthcare researches and practices. The widespread use of smartphones, the growth of mobile application development and the increasing demands of users have promoted the development, and usage of mHealth applications. In this work, a mHealth application called as ProvVacT is developed for tracking vaccination history. The main contribution of ProvVacT is that the application uses provenance information in order to trace the vaccination history. Provenance is used to refer to the origin of the information. Thus, it provides the trustworthiness of data. In this work, the ProvVacT application is based on an ontology-based provenance management approach in order to improve the quality, reliability, and reusability of vaccination data. Moreover, the vaccine information is also represented with an ontology-based approach.

Abstract Provenanc refers to the origin of information. Therefore, provenance is the metadata that record the history of data. As provenance is the derivation history of an object starting from its original source, the provenance information is used to analyse processes that are performed on an object and to track by whom these processes are performed. Thus, provenance shows the trustworthiness and quality of data. In a provenance management system in order to verify the trustworthy of provenance information, security needs must be also fulfilled. In this work, an ontology‐based privacy‐aware provenance management model is proposed. The proposed model is based on the Open Provenance Model, which is a common model for provenance. The proposed model aims to detect privacy violations, to reduce privacy risks by using permissions and prohibitions, and also to query the provenance data. The proposed model is implemented with Semantic Web technologies and demonstrated for the health care domain in order to preserve patients' privacy. Also, an infectious disease ontology and a vaccination ontology are integrated to the system in order to track the patients' vaccination history, to improve the quality of medical processes, the reliability of medical data, and the decision making in the health care domain.

Various organizations share sensitive personal data for data analysis. Therefore, sensitive information must be protected. For this purpose, privacy preservation has become a major issue along with the data disclosure in data publishing. Hence, an individual’s sensitive data must be indistinguishable after the data publishing. Data anonymization techniques perform various operations on data before it’s shared publicly. Also, data must be available for accurate data analysis when data is released. Therefore, differential privacy method which adds noise to query results is used. The purpose of data anonymization is to ensure that data cannot be misused even if data are stolen and to enhance the privacy of individuals. In this paper, an ontology-based approach is proposed to support privacy-preservation methods by integrating data anonymization techniques in order to develop a generic anonymization model. The proposed personalized privacy approach also considers individuals’ different privacy concerns and includes privacy preserving algorithms’ concepts.

Individual privacy protection in the released data sets has become an important issue in recent years. The release of microdata provides a significant information resource for researchers, whereas the release of person‐specific data poses a threat to individual privacy. Unfortunately, microdata could be linked with publicly available information to exactly re‐identify individuals’ identities. In order to relieve privacy concerns, data has to be protected with a privacy protection mechanism before its disclosure. The k ‐anonymity model is an important method in privacy protection to reduce the risk of re‐identification in microdata release. This model necessitates the indistinguishably of each tuple from at least k − 1 other tuples in the released data. While k ‐anonymity preserves the truthfulness of the released data, the privacy level of anonymisation is same for each individual. However, different individuals have different privacy needs in the real world. Thereby, personalisation plays an important role in supporting the notion of individual privacy protection. This study proposes a personalised anonymity model that provides distinct privacy levels for each individual by offering them to control their anonymity on the released data. To satisfy the personal anonymity requirements with low information loss, the authors introduce a clustering based algorithm.

Blood is the life in our vessels. It makes up the 8% of our body weight. Loss of blood from 750 ml to 2000 ml is enough to enter us to shock condition due to loss of circulating volume. In situations like these, urgent blood transfusion needs to be handled with extensive care. Not all emergency services may have haematology experts, a good helper application with blood transfusion rules that also determines the possible donors for each patient is necessary. In this paper, we present blood ontology that is an extension of FOAF ontology to serve as a core service of blood transfusion process. We had researched the possible blood transfusion rules of different health organization all over the world and highlight the blood transfusion rules. These rules are embedded to a ontology structure includes blood, FOAF and Relationship ontologies. This ontology structure not only serves as storage for patient and donor information, but also as a knowledge extraction structure to determine the possible donors and help doctors in treatment.

User profiles are personal images of social community users. Users store and share their documents and express themselves with their personal information. In social community, user may also need to describe herself with more than one image and more than one user profile. Today's user profiling methodologies do not support such multi-role based profile management. We develop a new profiling methodology to create user profiles and a profile based policy management model to use personal profiles with policies for better personalization and better social networking control. We present a framework to store and use social community policies based on personal profiles to increase the efficiency of user policy management