PPPQ-ANN is a hybrid FHE+TEE framework with product quantization that generates databases in under 2 hours and delivers over 50 QPS on million-scale datasets while preserving privacy.
Crypten: Secure multi-party computation meets machine learning
2 Pith papers cite this work, alongside 62 external citations. Polarity classification is still indexing.
2
Pith papers citing it
62
external citations · external index
fields
cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Benchmarking finds FHE faster than SMPC for regressions and simple dense networks (especially with GPUs), while SMPC performs better on complex CNN models.
citing papers explorer
-
Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment
PPPQ-ANN is a hybrid FHE+TEE framework with product quantization that generates databases in under 2 hours and delivers over 50 QPS on million-scale datasets while preserving privacy.
-
A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning
Benchmarking finds FHE faster than SMPC for regressions and simple dense networks (especially with GPUs), while SMPC performs better on complex CNN models.