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Haoqi Wu

4 accepted papers

2025

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

ICML 2025poster

Large Language Models (LLMs) have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen. Exis…

Cited by 0SourcePDFScholar
2025

ObCLIP: Oblivious CLoud-Device Hybrid Image Generation with Privacy Preservation

NeurIPS 2025poster

Diffusion Models have gained significant popularity due to their remarkable capabilities in image generation, albeit at the cost of intensive computation requirement. Meanwhile, despite their widespread deployment in inference services such as Midjourney, concerns about the potential leakage of sens…

Cited by 0SourceScholar
2024

Ditto: Quantization-aware Secure Inference of Transformers upon MPC

ICML 2024poster

Due to the rising privacy concerns on sensitive client data and trained models like Transformers, secure multi-party computation (MPC) techniques are employed to enable secure inference despite attendant overhead. Existing works attempt to reduce the overhead using more MPC-friendly non-linear funct…

2024

Nimbus: Secure and Efficient Two-Party Inference for Transformers

NeurIPS 2024poster

Transformer models have gained significant attention due to their power in machine learning tasks. Their extensive deployment has raised concerns about the potential leakage of sensitive information during inference. However, when being applied to Transformers, existing approaches based on secure tw…