ManaTEE: an Open-Source Private Data Analytics Framework with Confidential Computing

In this talk, we introduce ManaTEE, an open-source framework designed to enable private data analytics for public research. Private data holds immense value not only for businesses but also for critical research domains like public health, economics, social sciences, and civic engagement. However, leveraging such data for analytics comes with significant privacy risks. ManaTEE aims to address these challenges by integrating a set of Privacy Enhancing Techniques (PETs), including confidential computing, to safeguard data privacy without compromising usability. The framework provides an interactive interface through JupyterLab, ensuring an intuitive experience for researchers and data scientists. We will showcase how Trusted Execution Environments (TEEs) ensure both data confidentiality and execution integrity, fostering trust between data owners and analysts. Furthermore, we will highlight how confidential computing can offer additional properties such as proof of execution, enabling researchers to demonstrate the reproducibility and integrity of their results through attestation. Finally, we discuss how ManaTEE simplifies deployment across various confidential computing backends, making secure and private data analytics both accessible and scalable for diverse use cases.