# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 4 Comparative Literary Deductions of Surveyed Privacy Preserving Techniques
### Results of Literary Deductions
In previous sections, we discussed those solutions in detail and in this section, we investigated and presented the state-of-the-art methods, parameters, and metrics to evaluate the degree of privacy provided by these solutions. Numerous privacy-preserving solutions were comprehensively examined to analyze underlying experimental infrastructure utilized for the evaluation, the evaluation parameters used for performance analysis followed by the nature of the solution i.e., if it is a fundamental privacy solution or applied. The fundamental solution refers to the privacy preserving solutions that strengthen the blockchain privacy whereas the applied solution corresponds to solutions that leverage blockchain for strengthening privacy in other application scenarios. The findings are summarized in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Experimental Infrastructure** & **Evaluation Criteria/Parameters** & **Fundamental/Applied** \\ \hline \multirow{8}{*}{[106]} & Mining Nodes: 20 & \multirow{8}{*}{\begin{tabular}{c} \\ \end{tabular} } \\  & Wallet Nodes: 20 & \multirow{8}{*}{\begin{tabular}{c} \\ \end{tabular} } \\  & Transaction Frequency: 5 s & \multirow{8}{*}{\begin{tabular}{c} \\ \end{tabular} } \\  & Consensus: Proof of Work & & Request Processing Time \\  & Arduino MKR1000 32-bit & & Transaction Size & Applied (Pervasive \\  & ARM Cortex-M0 + MCU & & Block Creation Time & Computing) \\  & 32 KB of SRAM and 256 KB of flash & & \\  & Raspberry Pi Zero W with a 1 GHz single-core CPU & & \\  & and 512 MB RAM & & \\ \hline \multirow{8}{*}{[107]} & Programming Language: R-Programming Language & Privacy-Level Index (Pindex) & \multirow{8}{*}{\
\begin{tabular}{c} \\ Dissimilarity level (DISS) \\ Information Loss \\ \end{tabular} } \\  & System Software: Ubuntu 18.04 LTS with GPU & & \\  & Quadro P6000 & & \\  & RAM: 32-GB & & \\ \cline{1-1}  & Three test chains, (Kylin, Jungle, Local), Blockchain, & \multirow{8}{*}{
\begin{tabular}{c} \\ \end{tabular} } \\  & Cloud were used & & \\  & Over 100 tests performed & & \\ \cline{1-1}  & Alibaba Cloud 2 core & & \\ \cline{1-1}  & RAM: 8 GB & & \\ \cline{1-1}  & Storage: 100 G & & \\ \cline{1-1}  & System Software: Ubuntu 16.04 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of state-of-the-art blockchain privacy evaluation parameters. From the table, it is evident that most of the evaluations are based on time, throughput, and memory required. All these parameters are dependent on computational resources. This means that the better the hardware machine used, the better will be the performance of the evaluated technique. None of these parameters take into account the level of privacy provided by a solution. When Bitcoin [15], Ring CT [109], Zerocash [76] were introduced, each of these claimed to provide privacy protection to user identity and user assets. The performance results given also depicted the same. However, the attacks [111, 112, 113, 114] in later studies showed the vulnerabilities in proposed solutions, which when exploited, deanonymized the users for up to 90%. This is a highly significant number. Therefore, that makes it remarkably clear that computational performance-based experiments and proof-of-concept are not sufficient to judge the efficiency of a privacy preserving solution. This implies that more factors or parameters should be considered for evaluation. Another finding that we inferred from the survey is elaborated in deduction 4 given below:  
**Deduction 4:**_Another discovery to be highlighted here, is that most of the privacy preserving frameworks are deployed using Ethereum [115] platform with Solidity [116] as programming language and tested using official Ethereum test networks. This means that Ethereum is a better platform when it comes to programming privacy related applications._