We will explore new techniques and approaches including differential privacy to enable privacy-preserving machine learning and data analytics in the real world. We aim to design and develop a general framework to enable automatic data analytics query analysis and rewriting to ensure the query results are differentially private. We plan to explore different approaches for differentially-private deep learning. Our goal is to both provide practical real-world solutions for privacy-preserving machine learning and data analytics and deepen the theoretical understanding in this area.
How can we create a truly trustworthy secure enclave? It will require open source design and implementation and decentralized trust on its lifecycle management. Although many TEEs have been proposed by both industry (e.g., Intel SGX) and academia (e.g., Sanctum), no full-stack implementation has been open-sourced for use.
Keystone is an open-source project for building trusted execution environments (TEE) with secure hardware enclaves, based on the RISC-V architecture. Our goal is to build a secure and trustworthy open-source secure hardware enclave, accessible to everyone in industry and academia. Keystone introduces customizable TEE, a new paradigm of building TEE wherein both platform providers and enclave developers customize their TEE to have minimal trusted computing base (TCB), and be highly optimized for the resource usage of each application. This enables a lot of use cases of Keystone enclaves from embedded IoT application to machine learning.