When organizations or users need to share data, there is always a risk that sensitive information (like personal habits, locations, or health records) could be exposed. Even if names are removed, it’s often possible to re-identify people from patterns in the data. To address this, researchers have developed methods that allow data to be shared while still protecting privacy — for example, by adding small amounts of “noise” to the data, using cryptography so no single party sees the raw data, or training models without moving the original data. In this project, students will build a working software system that combines some of these methods into one platform. The system will let users upload and share data in a way that provides privacy protection while keeping the data useful for analysis. Students will design the back-end (to apply privacy protections and handle large datasets), the APIs (so different parties can exchange data securely), and a front-end dashboard (so users can easily set privacy options and view results). By the end, the team will deliver a complete, usable prototype that shows how sensitive data can be shared safely and responsibly in real-world scenarios.