论文标题
一种有效的基于哈希的合奏方法，用于协作异常检测
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection
论文作者
论文摘要
在协作异常检测中，多个参与者在不交换自己的数据的情况下交换了在分散设备上培训的本地检测器。协作异常检测的一个关键问题是有效地汇总了多个本地检测器，以形成全球检测器，而无需违反参与者数据的隐私并降低检测准确性。我们研究基于局部敏感的哈希集合方法来检测协作异常值，因为它们与差异化机制是可合并的，并且兼容。我们提出的LSH ITABLE非常简单，并且在许多真实世界数据集的集中式和分散的方案上，最近的合奏竞争对手胜过最新的合奏竞争对手。
In collaborative outlier detection, multiple participants exchange their local detectors trained on decentralized devices without exchanging their own data. A key problem of collaborative outlier detection is efficiently aggregating multiple local detectors to form a global detector without breaching the privacy of participants' data and degrading the detection accuracy. We study locality-sensitive hashing-based ensemble methods to detect collaborative outliers since they are mergeable and compatible with differentially private mechanisms. Our proposed LSH iTables is simple and outperforms recent ensemble competitors on centralized and decentralized scenarios over many real-world data sets.
