论文标题
SOK：让隐私游戏开始！机器学习中数据推理隐私的统一处理
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
论文作者
论文摘要
在生产中部署机器学习模型可以使对手推断有关培训数据的敏感信息。有大量的文献分析了不同类型的推理风险，从会员推理到重建攻击。受游戏成功（即概率实验）研究密码学中的安全性属性的启发，一些作者描述了使用基于游戏的样式在机器学习中的隐私推断风险。但是，对手能力和目标通常以从一个演示到另一个演示的方式微妙的方式陈述，这使得很难联系和构成结果。在本文中，我们提出了一个基于游戏的框架，以系统化机器学习中隐私推理风险的知识体系。我们使用此框架来（1）为推理风险的定义提供了统一的结构，（2）在定义之间正式建立已知关系，（3）揭示迄今未知关系，这些关系很难被发现。
Deploying machine learning models in production may allow adversaries to infer sensitive information about training data. There is a vast literature analyzing different types of inference risks, ranging from membership inference to reconstruction attacks. Inspired by the success of games (i.e., probabilistic experiments) to study security properties in cryptography, some authors describe privacy inference risks in machine learning using a similar game-based style. However, adversary capabilities and goals are often stated in subtly different ways from one presentation to the other, which makes it hard to relate and compose results. In this paper, we present a game-based framework to systematize the body of knowledge on privacy inference risks in machine learning. We use this framework to (1) provide a unifying structure for definitions of inference risks, (2) formally establish known relations among definitions, and (3) to uncover hitherto unknown relations that would have been difficult to spot otherwise.
