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papersSEP 10 04:00 UTC

New Paper Studies Selective Homomorphic Inference for Efficient Private ML

A research paper on arXiv examines selective homomorphic inference, an approach to running machine learning on private data using fully homomorphic encryption. Instead of evaluating an entire input under encryption, which is computationally expensive, the method applies FHE only to the sensitive region of interest. The work aims to make privacy-preserving inference more efficient and practical.

arXivHomomorphic Encryptionfully homomorphic encryptionprivacy-preserving-machine-learningprivate inferenceselective homomorphic inference

COVERAGE · 1 REPORT · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGEncrypt What Matters: When Selective Homomorphic Inference Is Efficient ↗SEP 10 04:00 UTC

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