System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Packed Shamir secret sharing yields up to 11x lower communication and 2.6x faster online runtime for secure deep learning inference versus prior Shamir-based methods.
citing papers explorer
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Beyond Latency: A System-Level Characterization of MPC and FHE for PPML
System-level evaluation reveals that network constraints and hardware costs, rather than raw latency, often dictate the optimal choice between MPC and FHE for privacy-preserving ML.
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High-Throughput and Scalable Secure Inference Protocols for Deep Learning with Packed Secret Sharing
Packed Shamir secret sharing yields up to 11x lower communication and 2.6x faster online runtime for secure deep learning inference versus prior Shamir-based methods.