# Visualization System for Transparency Requirement Analytics
## 2 Literature Review
### Visualization in Transparency Analytics: Related Work  
Because of its importance and benefits, visual analytics [12] are widely used in both theory and practice. Researchers in visual analytics typically concentrate on one of the following transparency requirements: data, process, policy, or relationships. Furthermore, the majority of research is focused on data privacy and cybersecurity visualization. Cybersecurity researchers prioritize network security by identifying potential vulnerabilities and attacks [13,14]. In order to assess the viability of key technologies, [PERSON] et al. [14] developed a prototype system for large-scale network topology visual analytics. They concentrated on computers and how data are processed, as well as how other users can attack them.  
Visual analytics researchers, on the other hand, focus on one of the transparency requirements for social networks on the Internet (Twitter, Facebook, etc.). [PERSON] et al. [15] used GraphProtector, a visual analytics tool, as an example. It is used to protect the privacy of social network users. They created databases containing individuals' personal information. They used a common network cleansing technique based on the K-anonymity model. GraphProtector is a new system that maintains privacy in network datasets by interactively synthesizing and applying various anonymization models.  
Furthermore, [PERSON] et al. [16] proposed a study of privacy in social media networks such as Facebook and Twitter, as well as the use of machine learning algorithms after collecting data from the users of these social media networks. This study is based on information gathered from social media users who participated in a survey but were not required to answer all questions. There were 250 participants in total, with the majority of them having multiple social media accounts (Facebook, Twitter, Reddit, etc.). They spent 11-20 h per week on these social media networks on average. Each user response containing text was analyzed using text analytical methods, and the responses were divided into three categories: identity, age, and gender.  
[PERSON] et al. [17] have contributed to the development of Privacy-Protecting Visualization. Using both data mining and data visualization techniques for representation purposes, they contributed primarily to the supervision of the anonymization procedure in the field of data preservation. The objective of their work is to reduce the likelihood of privacy issues arising from the widespread practice of data collection.  
A few policy privacy visualization systems are also proposed. They have poor static interfaces with no interactive elements, making them difficult for both experts and novices to comprehend. [PERSON] et al. [18,19], for example, represent a privacy policy system in which they focused on a model of privacy policy notation in which people serve as the primary representation. Other researchers use UML or similar tools (such as draw.io) to represent policies and privacy [20]. The same issue, however, with poor visualization and a lack of interactions, makes understanding the tool difficult and increases the time required for system analysis and building.  
As previously stated, transparency researchers prioritize the analysis of privacy data over the development of visualizations that aid them in understanding and making de cisions based on these data. Furthermore, visual analytics only address one of the transparency requirements, such as privacy, security, or stakeholders visualization. To the best of our knowledge, no effort has been made to address the visualization of all requirements. To propose a new visualization, we analyze and describe two Shield diagram representations or prototypes to determine their limitations and how we can improve the current representation.