# A Review of Blockchain-Based Secure Sharing of Healthcare Data
## 3 Blockchain in Healthcare Data
### Blockchain with Medical Federal Learning  
The privacy of healthcare data prevents data-driven machine learning from working [38]. Federated learning is a new AI technique that protects data privacy when building AI models. Federated learning enables multiple nodes to learn a model together publicly, and only the gradients and losses are transmitted between nodes, and not the data itself, which can protect the data well. However, the nodes need to transmit the data to the central institution for the next computation. Blockchain can be a good alternative to the central institution and avoids the dishonesty of the central structure [39]. Figure 4 shows a federated learning model where the blockchain replaces the central authority.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Blockchain Types** & **Data Encryption** & **Smart Contract** & **Key Point** \\ \hline
[33] & fabric & yes & no &
\begin{tabular}{c} Re-encryption, \\ anonymous sharing \\ \end{tabular} \\
[34] & public & yes & yes &
\begin{tabular}{c} Lamport merkle \\ digital signature \\ Encryption after \\ data classification \\ unique data \\ certificate storage \\ efficient digital \\ verification \\ mechanism \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of blockchain-based IOMT models.  
[PERSON] et al. use blockchain and off-chain techniques to protect the source data itself from tampering and unauthorized access [40]. The central gradient aggregator is replaced by the blockchain. At the end of aggregation, the hash of the encrypted global model is stored in the blockchain for further sharing. Malicious FL nodes may introduce toxic models, which may add bias to the training data. A monitoring mechanism is introduced to query the history and authenticity of the training process. Malicious nodes can be detected. The malicious nodes can be reduced by reputation score. Sharing computing power and data among nodes is an ideal situation, which is difficult to achieve in reality. Because the computing power and data between nodes are not the same. It is difficult to reach a consensus among nodes without profit. It is fairer to reward tokens based on the workload of nodes. A new fully decentralized healthcare architecture is proposed by [41]. The model is used to share distributed EMR between federal hospitals based on blockchain and MEC. To facilitate EMR sharing, they designed a new decentralized EMR storage on MEC servers using the InterPlanetary file system (IPFS) platform. In particular, an access authentication mechanism was developed using a blockchain-based smart contract to authenticate access at the edge of the network without any central authority. In the consensus phase, only some miners are selected for Byzantine protocol verification in each round. Although this approach reduces the overhead, it increases the risk of being attacked. The authors of [42] propose a blockchain-smart healthcare-based FL framework. This model provides the governance of the entire training process. An adaptive differential privacy algorithm is proposed to add an additional security layer to FL. The algorithm adapts the noise according to the training process, balancing privacy and model accuracy. Finally, an efficient consensus protocol based on gradient verification is designed to encourage reliable IoT devices and edge nodes to contribute their data and computational power to federation learning. Blockchain replaces centralized institutions that may be risky. This avoids the situation where a central node is evil, and each transaction is recorded on the chain, which enables timely detection of malicious nodes and provides oversight. Blockchain also has unique economic properties that can motivate nodes to participate in model learning by posting tokens.  
Figure 4: Blockchain-based Federal Learning for Healthcare.