| VISION | To provide actionable insights into how to optimize differential privacy implementations for various practical applications, ensuring a strong balance between security and data usefulness. |
| MISSION | Evaluate different differential privacy mechanisms. Evaluate differential privacy using a federated learning framework. Analyze the experimental results across different privacy budgets (epsilon values) and various differentially private mechanisms, including Laplace, Gaussian and Exponential. |
| TEAM | Mason White, Sarah Staples, Caden Zeltner, Michael Shyu |
| CLIENT | Dr. Honglu Jiang |
| SITE | n/a |