Award-Winning Research
Our founders have accrued more than 7000 academic citations combined through their peer-reviewed research work. Here are some canonical examples:
PrivaCT: Circuit Privacy in Constant Time for FHE Evaluation of Functions
Under Review
Novel approach to achieving circuit privacy in constant time for fully homomorphic encryption evaluation.
SecureRAG: End-to-End Secure Retrieval-Augmented Generation
Under Review
Secure retrieval-augmented generation system that maintains privacy throughout the entire pipeline.
CryptoFace: End-to-End Encrypted Face Recognition
CVPR 2025
The first end-to-end encrypted face recognition system with up to 8x speedup via FHE-aware network design.
Practical Biometric Search under Encryption: Meeting the NIST Runtime Requirement without Loss of Accuracy
IEEE Transactions on Biometrics, Behavior, and Identity Science 2025
A practical approach to biometric search under encryption that meets NIST runtime requirements without sacrificing accuracy.
AutoFHE: Automated Adaption of CNNs for Efficient Evaluation over FHE
USENIX Security 2024
An automated compiler for efficient CNNs over FHE, outperforming Zama and CryptoLab baselines.
Improved Multiplication-Free Biometric Recognition under Encryption
IEEE Transactions on Biometrics, Behavior, and Identity Science 2024
A novel approach to biometric recognition under encryption that eliminates the need for multiplication operations.
HERS: Homomorphically Encrypted Representation Search
TBIOM 2022
Breakthrough homomorphically encrypted biometric feature search algorithm scaled to 100+ million gallery size.
HEFT: Homomorphically Encrypted Fusion of Biometric Templates
IJCB 2022
The world's only homomorphically encrypted biometric template fusion algorithm.
Secure Face Matching using Fully Homomorphic Encryption
BTAS 2018
A first approach to secure face matching using fully homomorphic encryption.
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
ICCV 2017
A novel approach to privacy-preserving visual learning using doubly permuted homomorphic encryption.