论文标题
基于点的广义成员推理攻击的引导程序聚合
Bootstrap Aggregation for Point-based Generalized Membership Inference Attacks
论文作者
论文摘要
引入了一个有效的方案，该方案将广义成员推理攻击扩展到模型培训数据集中的每个点。我们的方法利用数据分区来为参考模型创建可变大小的培训集。然后，我们为每个培训示例训练一个攻击模型，用于基于每个点的输出的参考模型配置。这使我们能够量化每个培训数据点的成员推理攻击漏洞。使用这种方法，我们发现少量参考模型培训数据导致了更强烈的攻击。此外，参考模型不需要与目标模型相同，提供了其他攻击效率。即使对手没有完整的原始数据集，也可以由对手执行攻击。
An efficient scheme is introduced that extends the generalized membership inference attack to every point in a model's training data set. Our approach leverages data partitioning to create variable sized training sets for the reference models. We then train an attack model for every single training example for a reference model configuration based upon output for each individual point. This allows us to quantify the membership inference attack vulnerability of each training data point. Using this approach, we discovered that smaller amounts of reference model training data led to a stronger attack. Furthermore, the reference models do not need to be of the same architecture as the target model, providing additional attack efficiencies. The attack may also be performed by an adversary even when they do not have the complete original data set.
