## 4 SSHies: An Era of Research
### Blockchain and Smart Contracts-Based Techniques in HIE Systems: A Background
The system achieves high accuracy in stroke prediction using neural network technology. The authors of [99] proposed a framework for the secure storage and sharing of EMRs that involves five key entities: a patient, doctor, data requester (employing attribute-based encryption (ABE) to achieve fine-grained access control), IPFS, and the Ethereum blockchain network. The doctor engages with the patient to generate their medical data, deploys a smart contract on the blockchain, encrypts the medical record according to the patient's access policy, and uploads it to IPFS. The data requester initiates a medical record access request to the doctor, verifies their identity, and provides the necessary attributes. Upon creating a search token, the data requester invokes the smart contract, which verifies the user's identity and returns relevant search results. Then, the data requester retrieves transaction information from the blockchain, calculates the file location, downloads the encrypted medical record, and decrypts it if their attributes satisfy the access structure in the ciphertext. The authors of [100] proposed a secure protocol for cloud medical information using blockchain technology. The protocol involves a cloud server storing health data and a blockchain storing related data such as hash, address, and access tree. Consortium blockchain is applied for data integrity and scalability. Patients establish access structures, allowing only doctors who satisfy the structure to access patients' health data. BAN (Burrows-Adabi-Needham) logic is performed to prove mutual authentication. AVISPA (automated validation of internet security protocols and applications) is used to validate the protocol's safety. The protocol is efficient and has better safety compared to related protocols. Furthermore, it includes registration, authentication, data upload, treatment, and checkup. The authors of [101] proposed a new personal health records sharing scheme that combines cloud storage, searchable symmetric encryption, attribute-based encryption, and blockchain technology to achieve privacy protection, keyword search, fine-grained access control, and data integrity verification. The scheme manages and distributes private keys through blockchain, addressing the single-point-of-failure problem. Users can quickly verify the integrity of encrypted personal health records without interacting with the cloud server. The proposed scheme is secure, tamper-proof, privacy-protected, key management, fine-grained access control, data integrity verification, and keyword-searchable, making it feasible for practical use. The hash values of encrypted personal health records are stored on the blockchain, and the related index set is stored in the smart contract. The study [102] presented a novel fraud detection methodology for healthcare fraud using sequence mining concepts. The methodology provides a comprehensive understanding of recurring patient visits and extracts visit patterns, raising awareness among doctors and hospital management about possible medical services. It also enables service providers to take preventive actions in case of anomalous behavior. The architecture is divided into two views: a case view and a logical view. The methodology has been validated using transactional data from a local hospital over the last five years. It is efficient and capable of identifying fraudulent cases. The study uses a prefix span sequence mining approach and the Bayes rule to populate frequent and rare sequences in a sequence rule engine. The authors of [103] proposed a secure and efficient data management framework called EdgeMediChain for sharing health data. The framework consists of four layers: the end-user Layer, which consists of data generators and data requesters; the local edge-mining layer, which deploys local edge-mining pools; and the global blockchain layer, which operates as a blockchain database.