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Ye Dong

3 accepted papers

2026

SecP-Tuning: Efficient Privacy-Preserving Prompt Tuning for Large Language Models via MPC

ICLR 2026poster

Large Language Models (LLMs) have revolutionized numerous fields, yet their adaptation to specialized tasks in privacy-sensitive domains such as healthcare and finance remains constrained due to the scarcity of accessible training data caused by stringent privacy requirements. Secure Multi-party Com…

Cited by 0SourceScholar
2025

MPCache: MPC-Friendly KV Cache Eviction for Efficient Private LLM Inference

NeurIPS 2025poster

Private large language model (LLM) inference based on secure multi-party computation (MPC) achieves formal data privacy protection but suffers from significant latency overhead, especially for long input sequences. While key-value (KV) cache eviction and sparse attention algorithms have been propose…

Cited by 0SourceScholar
2022

DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints

CVPR 2022poster

Backdoor attack is a type of serious security threat to deep learning models.An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output i…

Cited by 97PDFScholar