The rapid expansion of healthcare data necessitates scalable, interoperable, and privacy-preserving mobile data management solutions. MedEForm addresses this by integrating the openEHR standard with Google Firebase to deliver a real-time, cloud-based electronic health record (EHR) platform optimized for Android devices. Utilizing Archetype Definition Language (ADL) models from the openEHR Clinical Knowledge Manager (CKM), MedEForm features a GUI generator that dynamically renders context-aware data-entry forms client-side, eliminating the need for app updates. Authentication is securely managed via Firebase Authentication, while encrypted user credentials and EHR data are stored in Firestore, Firebase’s NoSQL document store. A dedicated query module enables role-based execution of single-patient, multi-patient, and cohort-level queries across a 90,000-instance dataset. Firestore’s real-time synchronization, offline support, and fine-grained security rules enforce low-latency access with robust access control. For epidemiological analysis, MedEForm implements anonymization of demographic, clinical, and geospatial data at weekly intervals, storing these de-identified records in a separate Firestore collection. This architecture supports high-quality analytics while upholding stringent privacy guarantees. By unifying open standards, dynamic interface generation, and secure cloud infrastructure, MedEForm offers a modular and interoperable framework for mobile health data collection and analysis.

Summary The precise diagnosis and effective treatment of patients rely heavily on healthcare data. However, sharing healthcare information can be challenging due to the potential risks of unauthorized tampering and data leakage. To address these concerns and facilitate secure and efficient data access for stakeholders within and outside the healthcare system, this study introduces a patient‐enabled blockchain system (PEBS). Patient‐enabled blockchain system uses the Model View Controller (MVC) approach where the model manages the off‐chain and on‐chain data, the view is the user‐accessible module, and the controller acts as an interface between a user interface and storage layer. It enables patients to control their data by determining specific access permissions and executes various smart contracts for stakeholders' registration, authorization, data storage, query, and update operations. Patient‐enabled blockchain system incorporates Modified Proof‐of‐Authority (MPoA), which has been compared against various consensus algorithms such as Proof‐of‐Work (PoW), Proof‐of‐Authority (PoA), and Istanbul Byzantine Fault Tolerance (IBFT). Furthermore, the suggested system incorporates the utilization of the Interplanetary File System (IPFS) to address concerns related to performance and storage. We conducted an in‐depth analysis and comparison of the system's performance using key parameters such as transaction latency and throughput. Experiments are carried out using network sizes of 10 and 30, with transaction counts from 5 to 500. The experiments show that the highest latency for the proposed system is 58,105 ms, almost 4.8 times less than PoW, which is 283,575 and provides 2.7 times higher throughput (101 transactions per second) than PoW (38 transactions per second).

Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.

Migration is a complex process and involves many challenges like correct schema mapping, correct data transfer, indexing, and error fixing. The objective of data migration is to enhance the overall quality and usefulness of the data. Database migration is difficult to do manually, and numerous tools have been developed to simplify the complicated task. This paper outlines the existing data migration tools present in the market. We have classified the tools into two categories: 1. Academic research-based tools, 2. Industry-driven tools. Academic researchers developed and proposed Academic research-based tools, whereas Industry-driven tools are produced by many popular organizations like Google, Amazon, IBM, and Microsoft. Thus, this study aims to contribute to the state-of-the-art database migration field, an active area of research over the past decade. Additionally, it serves as a foundation for selecting and developing relational-to-NoSQL data migration tools. This paper proposes future research directions to facilitate the broader adoption of migration tools in Academia and Industry.

Summary In the Covid‐19 pandemic, information about the medical equipment such as personal protective equipment, ventilators, testing kits, oxygen cylinders, ICU beds, and patient diagnostic status is a black box for the patients. This article proposes a blockchain‐assisted Covid‐19 big data chain (CovidBChain) framework to handle the Covid‐19 data, which is of colossal size (volume), coming from different sources (variety) and generated at every time instance (velocity). CovidBChain is proposed to protect electronic health records and Covid‐19 equipment's information from illegal modification. CovidBChain provides transparency, access control, and integrity to Covid‐19 data. The status of critical equipment like ventilator, Covid‐19 beds, oxygen cylinder, and ICU status each such operation is integrated into the CovidBChain as a transaction. A prototype has been simulated using Ganache, Metamask, InterPlanetary File System, and Reactjs. The comparative assessment using proof‐of‐work (PoW) and proof‐of‐authority (PoA) deduces that the upload and retrieval time in PoA is less than PoW, while the transaction cost is more in PoW. The overhead of message exchange communication is reduced by a factor of in PoA as compared to the PoW approach. CovidBChain has been tested on the Ethereum official test network Ropsten for PoW and Goerli for PoA.

Abstract Recent years have witnessed the wide availability of an array of transactional datasets for mining and other research activities. A primary concern related to the public sharing of transactional datasets is identifying individuals whose data is being published. Data anonymization is a commonly utilized privacy preservation method for preventing user identification. However, the existing anonymization models such as ‐anonymity, ‐uncertainty, and (h, k, p)‐coherence for privacy preservation of transactional data do not provide complete protection from the various types of possible privacy attacks. Therefore, this article proposes a novel privacy model called (k, m, t)‐anonymity to effectively prevent identity and attribute disclosure as well as skewness attack on transactional data. A genetic algorithm‐based implementation of the model is also presented. The genetic algorithm clusters transactional data based on the similarity among the transactions for effective ‐anonymization with low information loss. The clustering algorithm simultaneously aims to minimize the skewness of data distribution in the obtained clusters for preventing skewness attack on anonymized data. Experimental results have verified that the (k, m, t)‐anonymity model ensures transactional data anonymization without significant information loss. The proposed privacy model is implemented using the proposed approach on two real‐world datasets (health domain and click‐stream data) and an enormous dataset generated synthetically (health domain consisting of 5,00,000 records). The relative error is less as compared to the relative privacy and disassociation technique for all test case scenarios. Hence, the proposed anonymization model maintains the data utility.

With the advancement in technology, the approach to learning has also been modified. “Standardization” and “One-size-fits-all” has become an outdated concept. To adjust to the changing learning approaches, e-learning came into being, but this was not as per the knowledge and intelligence of users. This created a hurdle in the achievement of better learning and acquisition of skills. This calls for the provision of personalization in e-learning. Successful implementation of personalized e-learning in the present education system will lead to better and faster learning by adapting as per the preferences and knowledge of students. The core idea behind this research is to make an application using Android, which provides a personalized and adaptable route of e-learning using Ant Colony Optimization and recommendations from similar peers. This research will cater to the needs of many students, and it will help in decreasing the time taken to complete any subject or course. It will also help in attaining better and efficient learning as the learning route is determined as per the user. Also, the collection of records of every user will help in improving efficiency and accuracy in the determination of the learning path. The developed app aiming for adaptative e-learning can act as a promising solution during the Covid-19 scenario.

Data anonymization is commonly utilized for the protection of an individual's identity when his personal or sensitive data is published. A well-known anonymization model to define the privacy of transactional data is the km-anonymity model. This model ensures that an adversary who knows up to m items of an individual cannot determine which record in the dataset corresponds to the individual with a probability greater than 1/k. However, the existing techniques generally rely on the presence of similarity between items in the dataset tuples to achieve km-anonymization and are not suitable when transactional data contains tuples without many common values. The authors refer to this type of transactional data as diverse transactional data and propose an algorithm, anonymization of diverse transactional data (ADT). ADT is based on slicing and generalization to achieve km-anonymity for diverse transactional data. ADT has been experimentally evaluated on two datasets, and it has been found that ADT yields higher privacy protection and causes a lower loss in data utility as compared to existing methods.

Ever changing behavior of Electronic Health Records (EHRs) demands a generic schema that can adopt any new knowledge. Moreover, the exponential growth of EHRs should be managed by eliminating the need of storing sparse values. Traditional approaches favor the adoption of the relational approach as a data model for storing data due to its simplicity, SQL query support, and availability of mature data analysis tools. However, the relational model falls short when a generic schema and eradicating sparseness are utterly required. To accommodate the requirements of generic schema and elimination of sparseness in EHRs, alternative models such as Entity Attribute Value (EAV), Dynamic Tables (DT), Optimized Entity Attribute Value (OEAV), and Optimized Column-oriented Model (OCOM) have been proposed at the logical level. These models utilize the vertical representation of data as opposed to the horizontal representation used by the relational model. Authors in current research considered 50K instances (as per relational data model) of standardized EHRs to critically analyze the impact of changing logical (i.e., EAV, DT, OEAV, and OCOM) and physical (i.e., row-oriented and column-oriented) structure of data on space acquired by dataset and time required for query execution. Further, authors favored the use of standardized EHRs to achieve semantic interoperability and thus, adopt the openEHR standard for experimentation purpose. It has been observed that the column-oriented storage approach is more space-efficient than the row-oriented storage approach. Also, the OCOM data model is the most space-efficient among all the data models followed by DT (using a column-oriented storage approach). On the contrary, DT, when stored in column-oriented databases, is found to be the most time-efficient among analyzed data models.