Jaguar replaces prime-modulus HE with power-of-two arithmetic to enable coefficient-domain convolution and local-shift truncation, reporting 2-3.7x lower latency than Cheetah and Rhombus on ResNet-18/50 and MobileNetV2.
In: Proceedings of the 2017 IE EE Symposium on Security and Privacy (SP), pp
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CHRONOS decouples cryptographic setup from training in federated learning via hardware enclaves and Shamir shares, reducing aggregation latency by up to 74% while resisting OS compromises and gradient inversion.
citing papers explorer
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Jaguar: Fast Private CNN Inference with Power-of-Two Homomorphic Arithmetic
Jaguar replaces prime-modulus HE with power-of-two arithmetic to enable coefficient-domain convolution and local-shift truncation, reporting 2-3.7x lower latency than Cheetah and Rhombus on ResNet-18/50 and MobileNetV2.
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CHRONOS: A Hardware-Assisted Phase-Decoupled Framework for Secure Federated Learning in IoT
CHRONOS decouples cryptographic setup from training in federated learning via hardware enclaves and Shamir shares, reducing aggregation latency by up to 74% while resisting OS compromises and gradient inversion.