## 2 Literature Review
By lengthening the keys in the Data Encryption Standard (DES), the Triple Data Encryption Standard (TDES) technique offers a way that is significantly easier to use while securing data privacy and preventing assaults. The experiment's findings demonstrate that the TDES technique is effective in securing and safeguarding substantial volumes of healthcare data stored in the cloud [27]. This research presents a unique encryption technique using Serpent, an Advanced Encryption Standard (AES), and elliptic curve cryptography to protect healthcare data in IoT-enabled healthcare infrastructure. The suggested hybrid encryption approach improves healthcare data security by integrating symmetric and asymmetric-based encryption techniques. This technique additionally makes use of an elliptic curve-based digital signature to ensure the data's integrity [28]. The Internet of medical things (IoMT) network receives massive amounts of big data, also known as health data, and registers a sizable number of patients and devices each day. To prevent misuse, this patient data should remain private and secure on the IoMT network. To achieve such data privacy and security, the interplanetary file system (IPFS) and a three-level/tier network have been proposed [29]. An enhanced architecture for safe and scalable IoT-based healthcare data transfer is based on the routing protocol. First, a variety of Internet of Things (IoT) devices, including wearables and sensors, collect health data. Utilizing data reduction and cleaning procedures, the raw data are preprocessed. Principal Component Analysis (PCA) and K-Nearest Neighbour (KNN) imputation are used to minimize the dimensionality of the data. The preprocessed data is used to extract the features using modified local binary patterns (MLBP). The FDT-RPL protocol for low-power and lossy networks enhances overall data transmission security by integrating the Butter Ant Optimization (BAO) algorithm with the fuzzy dynamic trust-based RPL algorithm [30]. None of these methods, therefore, will guarantee knowledge protection, which is necessary for both privacy and usefulness. To safeguard medical records, an effective anonymization approach is nevertheless required. Recent developments have shown that sensitive data, including proprietary knowledge, is kept, frequently by meta-heuristic algorithms. These methods are designed to generate an ideal key for the sanitization process. It is demonstrated that these algorithms produce superior results when compared to traditional methods. When compared to security, privacy is valued as a benefit, which is why it is important for both purchasing and selling in the business. Therefore, it is critical to strike a balance between security and privacy. A number of research frequently employed query and k-anonymity to address privacy concerns. However, these methods need a lot of computational resources and time. Our recommended approach offers a more reliable security system for cloud-based large data in healthcare. The remaining parts of the paper are laid out as follows: The proposed work is discussed in Section 3, Results and Discussions are presented in Section 4, and the conclusion and the work to come are discussed in Section 5.