1 citations · 1 across the 2 of their papers we have counts for
4 papers
HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption
Halil İbrahim Kanpak, Sinem Sav, Alptekin Küpçü
Many organizations adapt large pretrained models to their own tasks by fine-tuning on private data. Several of these parties often hold data for the same task and wish to fine-tune…
Static Bootstrap Placement for Encrypted Language Model Decoding
Halil Ibrahim Kanpak, Didem Unat
Language models increasingly serve prompts that carry private data, and secure inference under homomorphic encryption lets a client outsource the computation without revealing the…
A Taxonomy of Attacks and Defenses in Split Learning
Aqsa Shabbir, Halil İbrahim Kanpak, Alptekin Küpçü +1
Split Learning (SL) has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to serve…
CURE: Privacy-Preserving Split Learning Done Right
Halil Ibrahim Kanpak, Aqsa Shabbir, Esra Genç +2
Training deep neural networks often needs large datasets stored and processed in the cloud, and in sensitive fields like healthcare, these workflows must follow strict privacy rule…