# 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  
The results of the analysis are presented in the graph depicted in Figure 5. The graph shows yearly distribution of articles based on blockchain privacy that were taken into consideration, with respect to aforementioned classes. Figure 4: Survey methodology. \begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Articles are no more than 5 years old (i.e., published in range of 2017–2021) & Survey articles on blockchain privacy \\ Articles must be related to blockchain privacy preserving techniques & Privacy breaching attacks on blockchain networks \\ Articles must be written in English language & Grey literature (i.e., online blogs, etc.) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of privacy preserving techniques. From the graph obtained (Figure 5), we can see most of the studies surveyed used cryptography for privacy protection. The majority of these studies used simple data encryption techniques including Attribute Based Encryption [84], Content Extraction Signature [85; 86], RSA algorithm and others. The rest of them utilized ring signatures [73; 87], zero-knowledge proofs [88; 89] and other commonly known cryptographic techniques. Besides cryptographic techniques, a number of studies used machine learning approaches [90; 91] for preserving blockchain privacy, followed by a very low number of studies exploring smart contracts [92; 93; 94] for the task. Furthermore, most of the papers surveyed leveraged blockchain privacy mechanisms into various application areas that require protecting data privacy. These applications include ad-hoc vehicular networks [95; 96; 97], healthcare [98; 99; 100; 101], crowd-sourcing [102; 103], e-voting [104; 105] and more. Several IoT applications such as protecting sensor data, body area networks, vehicular parking systems were also identified as potential application areas that requiring greater privacy guarantees. A pie chart depicting these privacy centric applications found in literature is given as Figure 6.  
We derived a few deductions from the literary findings in this section. These findings are elaborated below:  
Figure 5: Latest trends in blockchain based privacy preserving solutions. Figure 6: Privacy centric applications in blockchain networks. **Deduction 1:**_From Table 1, we can infer that most of the experiments were based on the performance. However, stronger privacy guarantees are not directly proportional to utilization of lesser computational resources. Hence, performance-based experimentations and results cannot be completely relied on when considering privacy strength of a technique. This deduction is comprehensively studied and analyzed further in Section 5._  
**Deduction 2:**_Another deduction that was inferred from the articles studied was that the smart contracts (despite their abundant usage in blockchains) are not largely studied for privacy protection in comparison to cryptography. Smart contracts play a major role in development of the blockchain networks, hence exploiting their full potential will result in promising privacy protection in blockchains._  
**Deduction 3:**_As inferred from the literary surveyed articles, healthcare record management systems and supply chains, Internet of Things (IoT) based sensor data management systems, financial applications, and vehicular communication networks are the topmost privacy centric applications, followed by smart technologies, crowdsourcing, federated and deep learning data management and so on._  
Identification and Discussion on Evaluation Parameters and Criteria for Blockchain Privacy Preserving Techniques  
Due to the technology of blockchain having huge privacy concerns, extensive research is being conducted into this domain. Following which, numerous privacy-preserving solutions have been proposed in literature.