Pacifo framework enhances privacy for machine learning inference

Researchers have developed Pacifo, a framework designed to enhance the privacy and efficiency of machine learning inference services using fully homomorphic encryption (FHE). Pacifo adapts the ResNet20 model by partitioning layers and approximating ReLU functions, which significantly reduces the computational overhead and latency associated with FHE operations. This approach achieves a speedup of 3.7-21.7x compared to existing FHE implementations of ResNet20, while maintaining 95%-99% of the original model's accuracy. AI

IMPACT Improves privacy and efficiency for machine learning inference services, potentially enabling more secure deployment of AI applications.

RANK_REASON The cluster contains an academic paper detailing a new framework and model adaptation for machine learning inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• fully homomorphic encryption
• Hugging Face
• ResNet20

• paper
• infra
• safety

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