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Qiang Yan

4 accepted papers

2026

Secret-Protected Evolution for Differentially Private Synthetic Text Generation

ICLR 2026poster

Text data has become extremely valuable on large language models (LLMs) and even lead to general artificial intelligence (AGI). A lot of high-quality text in the real world is private and cannot be freely used due to privacy concerns. Therefore, differentially private (DP) synthetic text generation…

Cited by 0SourceScholar
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
2022

MGAD: Learning Descriptional Representation Distilled from Distributional Semantics for Unseen Entities

IJCAI 2022poster

Entity representation plays a central role in building effective entity retrieval models. Recent works propose to learn entity representations based on entity-centric contexts, which achieve SOTA performances on many tasks. However, these methods lead to poor representations for unseen entities sinc…

Cited by 0SourcePDFScholar