论文标题
编辑器：以数据为中心和个性化的防御推理攻击
Redactor: A Data-centric and Individualized Defense Against Inference Attacks
论文作者
论文摘要
随着各种信息因错误而公开获得，信息泄漏已成为一个关键问题，并且机器学习模型训练该数据以提供服务。结果，这种训练有素的模型可以很容易地记住一个人的私人信息。不幸的是，由于数据已经暴露于Web或第三方平台，因此删除信息不可能。此外，我们也不一定会控制其他当事方的标签过程和模型培训。在这种情况下，我们研究了目标虚假信息产生的问题，在这种问题中，目标是通过仅插入新数据来稀释数据，从而使模型更安全，更强大，以防止对特定目标（例如，一个人的概况）的推理攻击。我们的方法在输入空间中找到最接近目标的点，该点将标记为不同的类。由于我们无法控制标签过程，因此我们通过使用数据编程技术结合多个分类器的决策边界来保守地估算标签。我们的实验表明，概率决策边界可能是标签者的良好代理，并且我们的方法有效地防御了推理攻击，并且可以扩展到大数据。
Information leakage is becoming a critical problem as various information becomes publicly available by mistake, and machine learning models train on that data to provide services. As a result, one's private information could easily be memorized by such trained models. Unfortunately, deleting information is out of the question as the data is already exposed to the Web or third-party platforms. Moreover, we cannot necessarily control the labeling process and the model trainings by other parties either. In this setting, we study the problem of targeted disinformation generation where the goal is to dilute the data and thus make a model safer and more robust against inference attacks on a specific target (e.g., a person's profile) by only inserting new data. Our method finds the closest points to the target in the input space that will be labeled as a different class. Since we cannot control the labeling process, we instead conservatively estimate the labels probabilistically by combining decision boundaries of multiple classifiers using data programming techniques. Our experiments show that a probabilistic decision boundary can be a good proxy for labelers, and that our approach is effective in defending against inference attacks and can scale to large data.
