# Enhancing User Experience with Visual Controls for Local Differential Privacy
## 5 Privacy Control User Interface
### UI Design and Prototype Development  
We built an interactive prototype in Figma that demonstrates the user interface for adjusting privacy settings [22]. Figures 3 and 4 present our privacy control widget design featuring desktop and mobile views. The interface design language builds upon Amazon's Privacy Hub's foundation, chosen for its intuitive navigation and comprehensive privacy control explanations.  
Our interface design prioritizes accessibility without sacrificing technical rigor. By abstracting complex mathematical concepts while maintaining transparency about their effects, we help users understand privacy implications without requiring expertise in differential privacy. The interface achieves this balance by displaying immediate, practical consequences of privacy choices, thereby reducing cognitive load while ensuring informed decision-making.  
The interface presents a continuous spectrum of four privacy levels (as shown in Figure 5):  
* Minimal (Privacy Level 1, \(\varepsilon=1\)): Emphasizes full assistance with proactive automation.
* Basic (Privacy Level 3, \(\varepsilon=0.33\)): Balances automation with limited privacy protection.
* Balanced (Privacy Level 4, \(\varepsilon=0.25\)): Recommended setting for most users.
* Enhanced (Privacy Level 7, \(\varepsilon=0.14\)): Maximizes privacy through local processing only.  
Our choices for privacy levels (1, 3, 4, and 7) were primarily driven by visualization effectiveness and user comprehension. Through extensive experimentation, we observed that when \(\varepsilon\geq 1.0\), the privatized data points show nearly complete overlap with the original data, making it difficult to demonstrate meaningful privacy protection. When \(\varepsilon<0.1\), excessive data scattering occurs, obscuring the underlying patterns. Instead of using complex mathematical terminology, the interface translates differential privacy concepts into user-friendly terms, replacing \"Utility\" with \"Personalization\". This establishes a clear relationship: higher personalization corresponds to higher data utility requirements. The primary visualization displays the relationship between data points and personalization levels, with a \"Balanced Privacy\" threshold indicating the recommended trade-off point. We intentionally abstract specific data point numbers to help users understand relationships while preventing arbitrary numbers from influencing decisions.  
Figure 4: Privacy control widget with desktop view.  
To enhance user understanding, we incorporate a \"Data Examples\" table positioned on the right side of the interface. This educational element provides concrete before-and-after examples demonstrating how differential privacy processing affects user data. The table presents original command categories alongside their noise-added counterparts, using red highlighting to emphasize transformed data points. For example, a command initially categorized as \"music\" might transform to \"lights\" or \"none\" at higher privacy levels. This visualization illustrates how increased privacy through noise addition can alter command categorization specificity, making the abstract concept of differential privacy tangible through familiar examples. This proposed design approach empowers users to make informed decisions about their data privacy while understanding the practical implications for their device's functionality, achieving our goal of making the complex concept of differential privacy more tangible and actionable for the average user.