A Unified Framework for PETs
A major barrier to the adoption of privacy-preserving computing is usability. Currently, implementing systems that utilize FHE, TEEs, or ZKPs requires deep domain expertise in cryptography and hardware security. Software developers who simply want to build a secure application are forced to manually manage cryptographic keys, handle ciphertext memory limits, and rewrite their standard code into highly constrained formats.
ur ultimate vision is a unified platform that abstracts away this complexity. We aim to build intelligent compilers, automated frameworks, and hybrid architectures that allow developers to write “normal” unencrypted code, which the system automatically translates into secure, privacy-preserving executions. By integrating various PETs seamlessly, we can choose the right tool for the right job—balancing security, privacy, and speed autonomously.
Core Research Themes
We utilize AI as a proactive shield, focusing on three main lenses:
- “Privacy-Enhancing Compilers:” Developing automated tools that translate standard plaintext code into optimized cryptographic circuits or TEE-compatible binaries.
- “Unified PET Platforms:” Creating middleware that dynamically orchestrates different privacy techniques (combining FHE and TEEs) based on the specific security and performance requirements of the task.
- “Developer-Friendly Security:” Designing APIs and frameworks that hide mathematical complexity while mathematically guaranteeing data protection.
Key Sub-Topics & Keywords
To give you an idea of potential topics you may be interested in (but not bounded to):
- FHE Compilers and Auto-Vectorization**
- Hybrid TEE-FHE Architectures
- Secure Code Generation for AI Models
- End-to-End Encrypted System Design
Student Note: If you are a big-picture thinker who wants to build the fundamental tools, compilers, and platforms that will allow software engineers worldwide to easily write secure code, this field is for you.