# Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
## 3 Design Method
### Data Analysis
For example, among the 13 apps and 15 types of collected information, the SDK embedded in the \"REDnote\" app appeared most frequently (\"Huawei Push SDK\" appeared 15 times and \"Mobile SDK\" appeared 34 times). As users, we primarily rely on the information disclosed by developers in privacy policies. This makes it difficult to know where our data go and to determine whether the permissions we grant are necessary for the app's services or are being marketed and sold as currency between platforms. Through the investigation of previous studies and the analysis of collected data, we noticed that many users and researchers mentioned \"algorithms\". Users suspect they are being \"eavesdroped on\" also because platforms and operators use behavior-targeting strategies after obtaining user data. These strategies involve using algorithms to process users' behaviors (browsing, searching, purchasing, etc.) on websites or applications to push related advertisements. Reasons for Choosing the Algorithms  
In the data processing of social media platforms, the choice of specific algorithms directly impacts how user privacy data are utilized. Analyzing previously collected social media posts revealed that users' main privacy concerns focus on how platforms collect,  
Figure 2: Organization of user privacy policies from the five most popular social media platforms. analyze, and utilize their personal data. Based on these findings, we selected three representative algorithms, closely related to user privacy, for visualization research. Cluster Analysis: The Foundation of User Profiling. Cluster analysis algorithms are commonly used on social media platforms for user segmentation and profiling, directly addressing the issue of \"being categorized\", which concerns many users. This algorithm is a valuable tool in data science [27; 28] and plays a crucial role as a core element of data analysis. By analyzing data such as browsing habits, search history, and interaction behaviors, the algorithm groups users with similar characteristics. The primary reason for selecting this algorithm is that it provides an intuitive way to demonstrate how platforms interpret and categorize user behavior. It is also closely linked to the \"information cocoon\" phenomenon often mentioned in user feedback. Additionally, it forms the foundation for personalized services, significantly shaping how users access information. Collaborative Filtering: The Core of Recommendation Systems. Collaborative filtering algorithms are central to personalized recommendations and are closely linked to users' common sense of being \"monitored\". There are two primary types of collaborative filtering algorithms: user-based and item-based. As one of the most widely used recommendation algorithms [29], it explains why users receive suggestions based on the behavior of similar users. This method illustrates how platforms can deliver personalized recommendations without directly accessing users' private data. Association Rule Learning: Predicting Behavioral Patterns. Association rule learning, also known as market basket analysis (MBA), is a data mining technique that helps retailers understand buyers' purchasing behaviors, enabling them to make more informed decisions [30]. By analyzing user behavior patterns and predicting potential interests, this technique directly addresses users' concerns about platforms \"predicting their needs\". It shows how platforms use historical data to forecast user interests, how user data drive business decisions, and how users can better understand the data analysis behind personalized services. The selection of these algorithms for this study is based on two main considerations. First, they directly influence the personalized content users encounter, and their mechanisms are closely tied to user privacy. Second, they represent three key aspects of data utilization: data classification, association analysis, and personalized recommendations. This approach allows for a comprehensive exploration of the data processing workflow.