## 2 Literature Survey  
### Related Works  
In 2021, [PERSON] et al. [18] introduced privacy-preserving blockchain architecture (PPChain), and PPCChain's design has been changed in Ethereum. PPCChain's architecture allowed regulation to provide security. They specifically incorporated cryptographic primitives such as broadcast encryption and group signature into a workable byzantine fault tolerance consensus protocol with a separation mechanism to remove the transaction fee and mine for a reward instead of using the existing mining model. They offered in-depth security and privacy analysis and a performance study to demonstrate the usefulness of PPCchain. Examples from the food supply chain, sealed-bid auctions, and cryptocurrency described how the PPCChain might be used in regulation applications. In 2022, [PERSON] et al. [19] implemented an efficient architecture for secure misbehavior detection in lightweight IoMT tools in the \"artificial pancreas\" model (APS). The suggested method used deep learning, which protected privacy, and boosted security by integrating blockchain technology built on the Ethereum smart contract ecosystem. The efficacy of the developed system has been empirically tested for commensurate incentive schemes, exhaustiveness with compact findings, and an untraceable characteristic from a different neural deep learning technique. Consequently, the model has a high recall rate, demonstrating that it is almost completely capable of identifying harmful events in the case being studied. In 2022, [PERSON] et al. [20] designed a secure privacy preservation authentication mechanism for inter-constellation collaboration. They created both permanent and transient identities for each satellite to protect privacy. The permanent identity was used for inter-constellation collaboration, whereas the temporary identity was utilized for communication inside the constellation. For information exchange among cooperative satellite constellations, a consortium blockchain was introduced. A replica storage node mechanism has been suggested to enable effective authentication with minimal resources, where well-resourced satellites cache the duplicated data exchanged across the blockchain. A branch-and-bound approach has been used to address the integer programming issue of choosing the replica storage node. According to a security study, the suggested authentication technique was secure against various possible attacks, which included formal analysis using informal verification and BAN Logic. The suggested system provided efficiency in communication overheads, signaling, and processing with greater performance, based on a comparison between it and other privacy preservation schemes. Evaluations showed that the suggested onboard caching approach achieved minimal storage costs and communication delay. In 2022, [PERSON] et al. [21] implemented a privacy preservation model in smart healthcare using a blockchain-based federated learning method for preserving privacy, which used IoT cloud platforms to provide privacy and security. Scalable machine learning applications such as health-care use federated learning technologies. Users could also utilize a well-trained deep learning system without putting their private information in the cloud. Additionally, it covered the uses of federated learning in a smart city's distributed secure environment. In 2020, [PERSON] et al. [2020] implemented a blockchain-based privacy preservation system in the Ethereum network to provide better privacy preservation data. In that model, they used various mechanisms in the blockchain. Privacy protection and analyzing anonymity methods were used in digital currency. The aim of the encryption mechanism was focused on the privacy protection scheme. In 2022, [PERSON] et al. [2020] designed the proof of authority (PoA) consensus process, which required little computational power, and it has been implemented on a Raspberry Pi network. The elliptic integrated encryption process used a double-encryption process to protect the secrecy of data.