论文标题
有关机器学习和未来前景的差异隐私的调查
A Survey on Differential Privacy with Machine Learning and Future Outlook
论文作者
论文摘要
如今，机器学习模型和应用已变得越来越普遍。随着机器学习模型的开发和使用的迅速增加，人们对隐私的关注点已经上升。因此，保护数据免受泄漏和任何攻击存在合法的需求。可用于保护机器学习模型免受任何攻击和漏洞的最强，最普遍的隐私模型之一是差异隐私（DP）。 DP是对隐私的严格定义，可以保证对手无法可靠地预测数据集中是否包含特定参与者。它通过向数据注入噪声，是否对输入，输出，地面真相标签，目标功能，甚至向梯度减轻隐私问题并保护数据的梯度。为此，本调查论文介绍了分为两个主要类别（传统的机器学习模型与深度学习模型）的不同差异私有机器学习算法。此外，概述了机器学习算法的未来研究方向。
Nowadays, machine learning models and applications have become increasingly pervasive. With this rapid increase in the development and employment of machine learning models, a concern regarding privacy has risen. Thus, there is a legitimate need to protect the data from leaking and from any attacks. One of the strongest and most prevalent privacy models that can be used to protect machine learning models from any attacks and vulnerabilities is differential privacy (DP). DP is strict and rigid definition of privacy, where it can guarantee that an adversary is not capable to reliably predict if a specific participant is included in the dataset or not. It works by injecting a noise to the data whether to the inputs, the outputs, the ground truth labels, the objective functions, or even to the gradients to alleviate the privacy issue and protect the data. To this end, this survey paper presents different differentially private machine learning algorithms categorized into two main categories (traditional machine learning models vs. deep learning models). Moreover, future research directions for differential privacy with machine learning algorithms are outlined.
