论文标题
在高内在维度社区中检索脆弱性的替代证明
An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood
论文作者
论文摘要
本文研究了最近的邻居搜索的脆弱性，这是数据分析和机器学习中的关键工具。漏洞被评估为攻击者需要添加到数据集点以修改其邻居等级W.R.T.的相对扰动量。查询。该数量的统计分布来自简单的假设。六个大型数据集的实验将该模型验证为一些离群值，这些异常值在违反假设方面进行了解释。
This paper investigates the vulnerability of the nearest neighbors search, which is a pivotal tool in data analysis and machine learning. The vulnerability is gauged as the relative amount of perturbation that an attacker needs to add onto a dataset point in order to modify its neighbor rank w.r.t. a query. The statistical distribution of this quantity is derived from simple assumptions. Experiments on six large scale datasets validate this model up to some outliers which are explained in term of violations of the assumptions.
