Research Projects in Deep Learning and Security

Machine learning and data analytics compute statistics and build models from different users' (often sensitive) data, such as location, medical, and financial data. With the rise of ubiquitous sensing, personalization, and virtual assistants, users' privacy is at ever-increasing risk. Can we enable the power and utility of machine learning and data analytics while still ensuring users' privacy? What is the relationship between privacy-preservation and generalization and robustness in machine learning?
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.

Secure computation is a powerful abstraction, protecting the integrity and confidentiality of computations over sensitive data. There are already many applications for secure computing, and it will continue to grow in importance. Secure hardware enclaves provide a powerful solution to secure computation with little or no performance overhead over native computation. Hardware enclaves enable computation over confidential data, providing strong isolation from other applications, the operating system, and the host.
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.

Seaglass: Cryptographic constructions for secure, privacy-preserving distributed information sharing