Safeguarding the Intellectual Property (IP) of data has become critically
important as machine learning applications continue to proliferate, and their
success heavily relies on the quality of training data. While various
mechanisms exist to secure data during storage, transmission, and consumption,
fewer studies have been developed to detect whether they are already leaked for
model training without authorization. This issue is particularly challenging
due to the absence of information and control over the training process
conducted by potential attackers.
In this paper, we concentrate on the domain of tabular data and introduce a
novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to
detect leaked data that are used to train classification models. The core
concept behind \textsc{LDSS} involves injecting a small volume of synthetic
data--characterized by local shifts in class distribution--into the owner's
dataset. This enables the effective identification of models trained on leaked
data through model querying alone, as the synthetic data injection results in a
pronounced disparity in the predictions of models trained on leaked and
modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible
with a diverse range of classification models, such as Naive Bayes, Decision
Tree, and Random Forest. We have conducted extensive experiments on seven types
of classification models across five real-world datasets. The comprehensive
results affirm the reliability, robustness, fidelity, security, and efficiency
of \textsc{LDSS}.

为了保护数据的知识产权，在机器学习应用日益增多、且其成功与训练数据的质量密切相关的背景下，缺乏对未经授权的模型训练过程泄露数据的检测机制。本文针对表格数据领域，引入了一种名为局部分布偏移合成（LDSS）的新方法来检测训练分类模型所使用的泄露数据。LDSS 通过向拥有者的数据集注入一小部分具有局部类别分布偏移的合成数据，通过仅使用模型查询即可有效识别出训练于泄露和修改数据集的模型，且 LDSS 与朴素贝叶斯、决策树和随机森林等各种分类模型都兼容。在五个真实世界数据集上对七种分类模型进行了广泛实验，全面结果证实了 LDSS 的可靠性、稳健性、保真性、安全性和效率性。