Due to the rapid advancement of Large Language Model (LLM), the whole
community eagerly consumes any available text data in order to train the LLM.
Currently, large portion of the available text data are collected from
internet, which has been thought as a cheap source of the training data.
However, when people try to extend the LLM's capability to the personal related
domain, such as healthcare or education, the lack of public dataset in these
domains make the adaption of the LLM in such domains much slower. The reason of
lacking public available dataset in such domains is because they usually
contain personal sensitive information. In order to comply with privacy law,
the data in such domains need to be de-identified before any kind of
dissemination. It had been much research tried to address this problem for the
image or tabular data. However, there was limited research on the efficient and
general de-identification method for text data. Most of the method based on
human annotation or predefined category list. It usually can not be easily
adapted to specific domains. The goal of this proposal is to develop a text
de-identification framework, which can be easily adapted to the specific
domain, leverage the existing expert knowledge without further human
annotation. We propose an aspect-based utility-preserved de-identification
summarization framework, AspirinSum, by learning to align expert's aspect from
existing comment data, it can efficiently summarize the personal sensitive
document by extracting personal sensitive aspect related sub-sentence and
de-identify it by substituting it with similar aspect sub-sentence. We envision
that the de-identified text can then be used in data publishing, eventually
publishing our de-identified dataset for downstream task use.

提出了一个名为 AspirinSum 的基于方面的保用效率的文本去识别框架，通过学习来自现有评论数据的专家方面，能够通过提取与个人敏感方面相关的子句并用类似的方面子句替代来高效地总结个人敏感文件，并最终将去识别的文本用于数据发布，以便进行下游任务的使用。

AspirinSum: 一个基于方面的实用性保留去识别摘要框架

AspirinSum: an Aspect-based utility-preserved de-identification  Summarization framework

Open data plays a fundamental role in the 21th century by stimulating
economic growth and by enabling more transparent and inclusive societies.
However, it is always difficult to create new high-quality datasets with the
required privacy guarantees for many use cases. This paper aims at creating a
framework for releasing new open data while protecting the individuality of the
users through a strict definition of privacy called differential privacy.
Unlike previous work, this paper provides a framework for privacy preserving
data publishing that can be easily adapted to different use cases, from the
generation of time-series to continuous data, and discrete data; no previous
work has focused on the later class. Indeed, many use cases expose discrete
data or at least a combination between categorical and numerical values. Thanks
to the latest developments in deep learning and generative models, it is now
possible to model rich-semantic data maintaining both the original distribution
of the features and the correlations between them. The output of this framework
is a deep network, namely a generator, able to create new data on demand. We
demonstrate the efficiency of our approach on real datasets from the French
public administration and classic benchmark datasets.

本文提出了一种隐私保护的数据发布框架，该框架基于差分隐私，可以适应不同类型的数据，使用深度学习和生成模型来生成数据，具有良好的效率和实用性，并在法国公共管理和经典基准数据集上进行了验证。