Author

Seok-Won Hong

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Conference Jul 2026

Teacher-Anchored Worst-case Stability Distillation for Membership Privacy

Knowledge distillation (KD) enables efficient deployment of deep learning models. However, it can increase privacy risks by making the student more vulnerable to membership inference attacks (MIAs), as the student may inherit patterns specific to the teacher’s private training data. Existing KD-based methods against MIAs often rely heavily on reference data or require multiple teachers, which limits their practical applicability. To address this issue, we propose Teacher-Anchored Worstcase Stability Distillation (TASD), a practical single-teacher KD method for reducing membership inference risk. Experiments on CIFAR-10 show that TASD provides stronger resistance to MIAs than existing KD-based methods at lower computational cost.

Hyeon-Jin Jeong, Seok-Won Hong, Jae-Hwan Jeong et al. · International Conference on... · 0 citations

Preprint Jul 2026

Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components

GradLock is introduced, a novel training-time injection attack that stealthily injects sensitive training data directly into the model parameters and employs dynamic gradient locking to prevent payload degradation during the optimization process.

Jin-Seong Kim, Han-Ju Lee, Seok-Won Hong et al. · 0 citations