Summary of Differential Privacy For Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability, by Fatima Ezzeddine et al.
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
by Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti, Martin Gjoreski, Ihab Sbeity, Marc Langheinrich, Silvia Giordano
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of anomaly detection (AD) while ensuring explainability and privacy. They propose a novel approach that combines Explainable AI (XAI) through SHapley Additive exPlanations (SHAP) with differential privacy (DP). The authors evaluate different AD models on various datasets, investigating the trade-off between detection accuracy, explainability, and privacy costs. Results show that DP has a significant impact on detection accuracy and explainability, depending on both the dataset and AD model used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is like finding the odd one out in a group of numbers or pictures. It’s important for things like finance and healthcare to find unusual patterns quickly and correctly. But it’s also important that nobody else can see why something looks unusual. This paper finds a way to balance these two goals by using a special kind of AI called Explainable AI (XAI) and another thing called differential privacy (DP). They test different ways to do anomaly detection on various sets of data and find out how well it works, depending on the type of data and method used. |
Keywords
* Artificial intelligence * Anomaly detection
