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Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs. [ABSTRACT FROM AUTHOR]

Cloud storage is the ideal solution for outsourcing big data since the cloud can store a large amount of data. Cloud storage, on the other hand, raises additional problems about data duplication, fine-grained access control, and privacy, all of these factors are crucial for cloud large data storage. Data duplication approaches based on encrypted data schemes now available do not provide for fine-grained access control. This paper proposes a secure framework for managing data using rapid asymmetric maximum based dynamic size chunking and fuzzy logic for deduplication. Chunking, fingerprinting, hashing, and writing are the four main process of the proposed method. Initially, chunking is done to split the files into chunks. Rapid Asymmetric Maximum (RAM) based Dynamic Size Chunking (DSC) is used in the proposed method. These chunked files are then fingerprinted using hashing process for ensuring data authentication. Then B-tree indexing approach is used in the proposed method in order to keep the fingerprinted in an organized state. General Type2-Fuzzy logic is using Ant Lion Optimization (ALO) is used for detecting duplicate files in the documents. In the cloud storage platform, only non-duplicate documents are safely kept. The Triple Data Encryption Standard is used to do a security study before outsourcing non-duplicate data to a third-party cloud server. The total computation time of the proposed technique is 0.4 s in the inline phase and 0.04 s in the offline phase, and the deduplication ratio is 95% in the inline phase and 90% in the offline phase. This proposed deduplication approach requires less storage, which reduces memory use and processing time. [ABSTRACT FROM AUTHOR]