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PPIDM: Privacy-Preserving Inference for Diffusion Model in the Cloud

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成果类型:
期刊论文
作者:
Zhangdong Wang;Zhihuang Liu;Yuanjing Luo;Tongqing Zhou;Jiaohua Qin;...
作者机构:
[Jiaohua Qin] College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, China
[Zhangdong Wang; Zhihuang Liu; Tongqing Zhou; Zhiping Cai] College of Computer Science and Technology, National University of Defense Technology, Changsha, China
[Yuanjing Luo] College of Computer Science and Technology, National University of Defense Technology, Changsha, China<&wdkj&>College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, China
语种:
英文
期刊:
IEEE Transactions on Circuits and Systems for Video Technology
ISSN:
1051-8215
年:
2025
页码:
1-1
基金类别:
the Science and Technology Innovation Program of Hunan Province (Grant Number: 2022RC3061 and 2023RC3027) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62102425, 62172155 and 62472434)
机构署名:
本校为第一机构
院系归属:
计算机与信息工程学院
摘要:
Cloud environments enhance diffusion model efficiency but introduce privacy risks, including intellectual property theft and data breaches. As AI-generated images gain recognition as copyright-protected works, ensuring their security and intellectual property protection in cloud environments has become a pressing challenge. This paper addresses privacy protection in diffusion model inference under cloud environments, identifying two key characteristics—denoising-encryption antagonism and stepwise generative nature—that create challenges such ...

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