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

Privacy and explainability create competing demands in anomaly detection systems: The paper demonstrates that adding differential privacy protections to make AI systems more private simultaneously reduces both detection accuracy and the quality of explanations users receive, forcing organizations to choose between these important safeguards.

The privacy cost varies significantly depending on the specific dataset and detection method used: Rather than a one-size-fits-all impact, the research found that the trade-off between privacy and performance "depends on both the dataset and the considered AD model," suggesting organizations need to evaluate these trade-offs for their particular use cases in finance, healthcare, and other sensitive domains.

Explainability itself is affected by which anomaly detection algorithm is chosen: The paper notes that "the visual interpretation of explanations is also influenced by the choice of the AD algorithm," meaning that decisions about model selection have downstream effects on whether stakeholders can actually understand why the system flagged something as anomalous.

Organizations cannot assume they can have strong privacy protections without accepting performance trade-offs: The findings show that differential privacy enforcement "has a significant impact on detection accuracy and explainability," indicating that claims of maintaining full system performance while adding privacy protections should be viewed skeptically in anomaly detection applications.