Minxuan Zhou
Illinois Institute of Technology, IL·Computer Science
Machine learning, biomedical analysis and many other data-heavy applications send data to the cloud for computing. Sharing that data can leak sensitive information, which can lead to identity theft and financial loss.
- Fields
Computer Science
Engineering
- Topics
fully homomorphic encryption
privacy-preserving computing
encrypted computation
- Grants
- 1 known grant, through Jun 2027
What they work on
- Design a domain-specific programming language that makes it easier to write general fully homomorphic encryption (FHE) applications across different algorithmic schemes.
- Build an optimized compiler that turns FHE programs into high-performance code for various emerging hardware platforms, making encrypted computation portable and fast.
- Integrate applications, programming languages and computer systems into an end-to-end framework for practical privacy-preserving computing on real systems.
More about the research
Fully homomorphic encryption (FHE) offers a fix: it lets a computer perform calculations directly on encrypted data, so the data never has to be decrypted in transit or in the cloud. FHE is also designed to resist attacks from future quantum computers. It is hard to use in practice, however. Programmers must translate ordinary data and operations into restricted encrypted formats using several complex algorithmic schemes. They must also adapt and tune the resulting programs for different hardware so they run fast enough.
Why it matters
Easier and faster encrypted computation could let hospitals, businesses and other data owners use cloud services for tasks such as machine learning, biomedical analysis and private information retrieval without exposing sensitive data. This could reduce the risk of identity theft, financial loss and other harms from data leaks.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Emma Tosch
Northeastern University, MA·Computer Science
Design a domain-specific language for software experiments that enforces experimental-design rules on hypotheses, analyses and treatment assignment, so that experiments are correct by construction.
Aleksandar Kuzmanovic
Northwestern University, IL·Computer Science
Co-design Private Information Retrieval (PIR) protocols, based on cryptography such as homomorphic encryption, that let a DNS server resolve domain names without learning which name was requested.
Milijana Surbatovich
University of Maryland, MD·Computer Science·Early-career
Develop the first formal model of an intermittence-safe operating system, with defined abstraction layers that separate tiers of trust and allow each component to be verified on its own.
The school
Illinois Institute of Technology: 5 labs on PI Pages, $38M a year on research, 76 research doctorates a year.
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