## 2 Literature Survey
### Statement of Problem
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Techniques** & **Features** & **Disadvantages** \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{PPCChain} & \(\bullet\) & It provides qualitative security and efficient privacy over personal data. & \(\bullet\) & It is not developed for attaining conflicting parameters such as regulation and confidentiality. Anonymity and transparency. \\  & & & It ignores the correlation between variables to enhance the training performance. & \(\bullet\) & It is computationally expensive. \\ \hline \multirow{4}{*}{[PERSON] et al. [19]} & \multirow{4}{*}{BiLSTM} & \(\bullet\) & It significantly increases the storage cost. & \(\bullet\) & It requires more training time and is a slower process than other convolutional techniques. \\  & & \(\bullet\) & It achieves higher prediction accuracy, precision and \(\mathrm{f1}\) score. & \(\bullet\) & It does not contain expensive hardware to do complex mathematical calculations. \\ \hline \multirow{4}{*}{[PERSON] et al. [20]} & \multirow{4}{*}{SGINs} & \(\bullet\) & It gives a high percentage of false positive rates. & \(\bullet\) & It does not access the service anywhere. \\  & & \(\bullet\) & The board caching scheme achieves low storage costs and low communication latency. & \(\bullet\) & It does not provide any security system for communication and privacy protection, so it suffers slightly from the security issue. \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & \multirow{4}{*}{Federated Learning} & \multirow{4}{*}{Learning} & \(\bullet\) & It achieves more functionality attributes regarding the efficiency in signaling, communication overheads, and computation. & \(\bullet\) & The efficiency is low when compressing the massive number of devices in the security system. \\  & & \(\bullet\) & It achieves efficient authentication with replica storage and limited resources. & \(\bullet\) & The federated communication system is very expensive. \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & \multirow{4}{*}{Data encryption} & \(\bullet\) & It efficiently achieves strict rights management, mainly used for several applications. & \(\bullet\) & It provides less computation for compatibility. \\  & & \(\bullet\) & It increases the integrity of the data, and it is very cheap to implement. & \(\bullet\) & The scalability is very low. \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & \multirow{4}{*}{Raspberry Pi network} & \(\bullet\) & The implementation cost is low. & \(\bullet\) & To handle a large amount of data, more storage is needed in this network. \\  & & \(\bullet\) & The model solution is more feasible and reliable. & \(\bullet\) & It does not provide a proper balance between connectivity and storage requirements. \\ \hline \multirow{4}{*}{[PERSON] et al. [24]} & \multirow{4}{*}{peer-to-peer} & \(\bullet\) & It improves computation rationality and identity anonymity. & \(\bullet\) & The data confidentiality is very poor. \\  & & \(\bullet\) & It is easy to set up the client data and does not need special knowledge. & \(\bullet\) & The file resources do not centrally organize, so it takes more time. \\ \hline \multirow{4}{*}{[PERSON] et al. [25]} & \multirow{4}{*}{EdDSA} & \(\bullet\) & It improves the immutability and transparency to enhance the privacy system highly. & \(\bullet\) & While using enormous data, the private key is sometimes leaked. \\  & & \(\bullet\) & It reduces the computational complexity of the decentralization algorithms. & \(\bullet\) & It does not support merging complex data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features and disadvantages of the existing blockchain-based privacy preservation techniques with blockchain technology in Ethereum.