Junbum Shin — accepted AI-conference papers

2025

IDFace: Face Template Protection for Efficient and Secure Identification

Sunpill Kim, Seunghun Paik, Chanwoo Hwang, Dongsoo Kim, Junbum Shin, Jae Hong Seo

ICCV 2025poster

As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such…

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2023

HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption

Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee

ICML 2023oral

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…

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