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HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
March 22, 2024, 4:42 a.m. | Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee
cs.LG updates on arXiv.org arxiv.org
Abstract: Transfer learning is a de facto standard method for efficiently training machine learning models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datasets. Although numerous previous studies proposed to use homomorphic encryption to resolve the data privacy issue in transfer learning in the machine learning as a service setting, most of them only focused on encrypted inference. In this study, we present HETAL, an efficient Homomorphic Encryption …
abstract arxiv classification cs.cr cs.lg data data privacy datasets encryption fine-tuning homomorphic encryption issue large datasets machine machine learning machine learning models privacy standard studies training transfer transfer learning type
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