# Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
## 3 Design Method
### Visual Transformation
#### 3.3.2 Visualization  
Through previous data collection and analysis, we found that personal information is like interstellar matter in the universe, either attracted or altered. Its data transmission process is akin to entering \"wormholes\" and \"black boxes\", where no one knows what changes might occur or where the data might go. The black-box operation of social media platform algorithms exacerbates this opacity. These wormholes and black boxes constitute a significant part of the current electronic information ecosystem. Therefore, to better disclose the principles of algorithms to users, we use the metaphor of roaming the interstellar universe to visualize key algorithms in the privacy data transmission process, namely clustering analysis, collaborative filtering, and association rule learning.  
Clustering analysis groups data objects based on their descriptions and relationships found in the data. The goal is to make objects within the same group similar (related) to each other, while objects in different groups are dissimilar (unrelated). Clusters obtained through clustering can be represented by their centers, sizes, densities, and descriptions.  
In the planetary metaphor, clustering analysis is like grouping similar types of planets into one galaxy. For example, during a user's interstellar adventure, if they see planets  
Figure 3: The basic process of design methodology, when combined with the wormhole metaphor, establishes the foundation for this visualization’s metaphorical logic.  
with rings, monochrome planets, and two-tone planets, they might group all ringed planets into one galaxy, all monochrome planets into another, and all two-tone planets into a third. Each galaxy represents a \"cluster\". A visualization of this metaphor can be seen in Figure 4. This metaphor clusters based on the type of planets (i.e., \"features\"). In user behavior data analysis, users are categorized based on their \"features\", which could be their shopping habits, the ads they click on, or the time they spend on a website.  
Collaborative filtering is divided into two types: user-based collaborative filtering (UserCF) and item-based collaborative filtering (ItemCF). UserCF analyzes the similarity between users to find a group of users most similar to the target user. It then predicts the target user's preferences for unknown items based on the behavior of these similar users. ItemCF, on the other hand, analyzes the similarity between items to find a group of items most similar to the target item. It then predicts the target user's preferences for unknown items based on their preferences for these similar items.  
In the visualization of collaborative filtering algorithms, planets liked by friends, planets liked by similar space explorers, and planets browsed are more likely to pass through layers of filters to appear before the user, as shown in Figure 5.  
Association rule learning is a key technique in unsupervised learning algorithms used to discover associations between itemsets in a dataset. It identifies association rules by analyzing the frequency and relationships of itemsets within the dataset.  
In this visualization design, during their cosmic exploration journey, as shown in Figure 6, users may notice that whenever they find a central gaseous planet, a large gaseous planet is often nearby. Similarly, whenever users see a glowing planet, there is likely another planet with a similar structure nearby. This is less common with other types of planets; for example, ringed planets are usually found next to distinctly layered planets. These association rules are valuable as they help us explore the universe more effectively and find rare resources. It is similar to how a great detective follows traces or how one finds patterns and reasons in games like Sudoku or Minesweeper.  
Figure 5: Visual diagram of collaborative filtering algorithm: user-centered filtering of content.