Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability

arXiv:2404.06144v1 Announce Type: new
Abstract: Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expected pattern of the majority of the data. Such a process finds wide application in various fields, such as finance and healthcare. While the primary objective of AD is to yield high detection accuracy, the requirements of explainability and privacy are also paramount. The first ensures the transparency of the …

abstract anomaly anomaly detection application arxiv cs.ai cs.lg data dataset detection differential differential privacy explainability fields outlier privacy process statistical trade trade-off type