In an era of increasing privacy concerns for our online presence, we propose
that the decision to appear in a piece of content should only belong to the
owner of the body. Although some automatic approaches for full-body
anonymization have been proposed, human-guided anonymization can adapt to
various contexts, such as cultural norms, personal relations, esthetic
concerns, and security issues. ''My Body My Choice'' (MBMC) enables physical
and adversarial anonymization by removal and swapping approaches aimed for four
tasks, designed by single or multi, ControlNet or GAN modules, combining
several diffusion models. We evaluate anonymization on seven datasets; compare
with SOTA inpainting and anonymization methods; evaluate by image, adversarial,
and generative metrics; and conduct reidentification experiments.

在越来越多关注在线私密性的时代，我们提出内容出现与否的决定应仅由身体的主人决定。尽管已经提出了一些全身匿名化的自动方法，但人类引导的匿名化可以适应各种情境，如文化规范、个人关系、审美考虑和安全问题。"My Body My Choice"（MBMC）通过移除和交换方法实现身体和对抗匿名化，旨在进行四项任务，由单个或多个 ControlNet 或 GAN 模块设计，并结合几种扩散模型。我们对七个数据集进行了匿名化评估；与 SOTA 图像修复和匿名化方法进行了比较；通过图像、对抗和生成度量进行了评估；并进行了重新识别实验。

我身体我选择：以人为本的全身匿名化

My Body My Choice: Human-Centric Full-Body Anonymization

Recent work in privacy research on large language models has shown that they
achieve near human-level performance at inferring personal data from real-world
online texts. With consistently increasing model capabilities, existing text
anonymization methods are currently lacking behind regulatory requirements and
adversarial threats. This raises the question of how individuals can
effectively protect their personal data in sharing online texts. In this work,
we take two steps to answer this question: We first present a new setting for
evaluating anonymizations in the face of adversarial LLMs inferences, allowing
for a natural measurement of anonymization performance while remedying some of
the shortcomings of previous metrics. We then present our LLM-based adversarial
anonymization framework leveraging the strong inferential capabilities of LLMs
to inform our anonymization procedure. In our experimental evaluation, we show
on real-world and synthetic online texts how adversarial anonymization
outperforms current industry-grade anonymizers both in terms of the resulting
utility and privacy.

近期在隐私研究中，大型语言模型在推断真实世界在线文本中的个人数据方面已经达到接近人类水平的性能。在存在不断增长的模型能力的同时，现有的文本匿名化方法目前无法满足监管要求和对抗威胁。这引发了一个问题，即个人如何能够有效地在分享在线文本时保护自己的个人数据。本研究分两步回答这个问题：首先，我们提出了一个新的环境，在对抗性大型语言模型推理的情况下评估匿名化性能，从而在纠正以前的指标缺陷的同时，实现对匿名化性能的自然度量。然后，我们提出了基于大型语言模型的对抗性匿名化框架，利用大型语言模型的强大推理能力来指导我们的匿名化过程。在我们的实验评估中，我们展示了在真实世界和合成在线文本中，对抗性匿名化在结果效用和隐私方面均优于目前的工业级匿名化工具。