Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks — Paper

Abstract

Diffusion models have achieved remarkable success in generative tasks across various areas, however their training process raises significant privacy concerns, particularly under membership inference attacks (MIAs). Prior studies on privacy-preserving of diffusion models fail to balance privacy, utility, and efficiency. To address this gap, we propose a novel framework of privacy-preserving diffusion models, Adaptive Diffusion Freezing (ADF), which can defend against MIAs with better trade-off. By leveraging cross-timestep adaptive freezing training, ADF explicitly control the participation of different data subsets across diffusion timesteps via a mask matrix, which reduces the over-memorization and leads to more uniform model behaviors between member and nonmember samples. To construct a freezing mask matrix that effectively reduce membership leakage without unnecessarily harming generation quality, we introduce a pretraining-based risk-aware freezing policy to estimate MIA risk based on memorization tendency, and suppress the contribution of the subset-timestep pairs with higher risk. Evaluations on multiple datasets demonstrate that ADF provides effective defense performance as well as state-of-the-art privacy-utility-efficiency trade-off performance compared to various baselines.

Authors 3

Jialu Guo, Xiao Han, Junjie Wu

Specification

Official page: arxiv.org/abs/2609.10608
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
arXiv id: 2609.10608
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Categories: cs.CR, cs.LG
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
PDF: arxiv.org/pdf/2609.10608
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Primary category: cs.CR
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 1 h agohigh

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T19

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1 h ago

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Authors: Jialu Guo, Xiao Han, Junjie Wu

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Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2609.10608	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperAdaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks
New paper: Adaptive Diffusion Freezing: Privacy-preserving Diffusion Models Against Membership Inference Attacks
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	1 h ago	1

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