# Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
## 1 Introduction
The survey revealed that users' primary data privacy concerns include the specific methods platforms use to collect and handle user data, the security of data in various usage scenarios, and the potential for misuse. However, most algorithm visualizations are intended for professionals, aiming to convey and optimize specialized knowledge [8]. Many existing studies on data privacy education aim to raise users' awareness and teach security and privacy concepts effectively through serious games [9], card-based methods [10], and similar approaches. In our experience, designers rarely attempt to make complex algorithms more accessible, and users seldom have the chance to understand how their online data flows clearly. The high technical barriers create a dilemma between concern and action for the average user. This limits independent thinking about data privacy and contributes to a lack of design insights in this field, presenting an opportunity for further design and research. Therefore, we aim to conduct an empirical study on Visualization of data flow process and related algorithms, focusing on the perspective of ordinary users to summarize and represent the circulation and processing of user data on social media platforms. This approach seeks to raise user awareness of the flow of their private data. First, the study designed a web scraping and analysis process for the privacy policies of five social media applications and the data from 13 third-party SDKs. Following that, we combined the results of data analysis and related algorithms (clustering analysis, collaborative filtering, and association rule learning) with a poetic metaphorical data visualization method, creating a system of metaphorical visual symbols. Finally, we invited 10 users to participate in an experiment to experience and discuss the visualization design and its themes. Building on the aforementioned context, our research lies at the intersection of three fields: data visualization, user experience design, and privacy data science education. We focus on how innovative visualization and interactive methods can help everyday users understand and navigate complex data privacy concepts. The primary contribution of our work is not in technical expression, but in providing a novel methodological framework for data privacy education through design innovation. Our target audience primarily consists of general internet users interested in protecting personal data privacy but lacking specialized knowledge. Additionally, our research offers valuable insights for privacy educators, data visualization researchers, and platform designers. The main contributions of this study are as follows:We propose a metaphor-based visualization framework that translates complex data privacy mechanisms into accessible visual narratives, specifically targeting non-expert users' understanding of algorithmic data processing on social media platforms. 2. We develop a systematic transformation framework that converts technical privacy concepts into visual metaphors through three interconnected stages: (1) data flow analysis of privacy policies and SDK sharing patterns, (2) algorithmic principle deconstruction focusing on clustering, filtering and association learning, and (3) interactive visualization design validated through user studies. 3. Through empirical evaluation with users, we demonstrate how metaphor-based visualization can (1) increase user comprehension of privacy-related algorithms, (2) motivate users to reconsider their privacy decisions, and (3) provide insights for improving privacy policy presentation in social media platforms. Additionally, we identify key challenges and opportunities in designing privacy-aware interfaces that balance user understanding with technical accuracy. This paper is organized as follows. We begin by reviewing related work in three areas: the disclosure of privacy policies, users' concerns about data security, and research on algorithms and algorithm visualization.