Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
This work addresses privacy and transparency challenges in anomaly detection for fields like finance and healthcare, but it is incremental as it evaluates existing methods rather than introducing new ones.
The paper tackled the trade-off between privacy and explainability in anomaly detection by applying differential privacy and SHAP explanations, finding that privacy enforcement significantly reduces accuracy and explainability, with impacts varying by dataset and model.
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 AD process, while the second guarantees that no sensitive information is leaked to untrusted parties. In this work, we exploit the trade-off of applying Explainable AI (XAI) through SHapley Additive exPlanations (SHAP) and differential privacy (DP). We perform AD with different models and on various datasets, and we thoroughly evaluate the cost of privacy in terms of decreased accuracy and explainability. Our results show that the enforcement of privacy through DP has a significant impact on detection accuracy and explainability, which depends on both the dataset and the considered AD model. We further show that the visual interpretation of explanations is also influenced by the choice of the AD algorithm.