论文标题
机器学习中隐私攻击的调查
A Survey of Privacy Attacks in Machine Learning
论文作者
论文摘要
随着机器学习的广泛使用，研究其对安全性的影响和隐私的需求变得更加紧迫。尽管在过去的几年中，隐私工作的工作一直在稳步增长，但对机器学习的隐私方面的研究却少于安全方面。我们在这项研究中的贡献是对过去七年来与机器学习的隐私攻击有关的40多种论文的分析。我们提出了一种攻击分类法，以及一个威胁模型，该模型允许根据对抗性知识和受到攻击的资产对不同的攻击进行分类。提出了对隐私泄漏原因的初步探索，以及对不同攻击的详细分析。最后，我们概述了最常见的防御措施，并讨论了我们分析过程中确定的开放问题和未来方向。
As machine learning becomes more widely used, the need to study its implications in security and privacy becomes more urgent. Although the body of work in privacy has been steadily growing over the past few years, research on the privacy aspects of machine learning has received less focus than the security aspects. Our contribution in this research is an analysis of more than 40 papers related to privacy attacks against machine learning that have been published during the past seven years. We propose an attack taxonomy, together with a threat model that allows the categorization of different attacks based on the adversarial knowledge, and the assets under attack. An initial exploration of the causes of privacy leaks is presented, as well as a detailed analysis of the different attacks. Finally, we present an overview of the most commonly proposed defenses and a discussion of the open problems and future directions identified during our analysis.
