Summary of Explainable Differential Privacy-hyperdimensional Computing For Balancing Privacy and Transparency in Additive Manufacturing Monitoring, by Fardin Jalil Piran and Prathyush P. Poduval and Hamza Errahmouni Barkam and Mohsen Imani and Farhad Imani
Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring
by Fardin Jalil Piran, Prathyush P. Poduval, Hamza Errahmouni Barkam, Mohsen Imani, Farhad Imani
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models integrated with in-situ sensing offer transformative solutions for defect detection in Additive Manufacturing (AM), but this integration brings critical challenges in safeguarding sensitive data. The study introduces the Differential Privacy-Hyperdimensional Computing (DP-HD) framework, a novel approach combining Explainable AI (XAI) and vector symbolic paradigms to quantify and predict noise effects on accuracy using a Signal-to-Noise Ratio (SNR) metric. The framework has been validated using real-world AM data, demonstrating its applicability to industrial environments. Experimental results demonstrate DP-HD’s capability to achieve state-of-the-art accuracy (94.43%) with robust privacy protections in anomaly detection for AM, even under significant noise conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are super helpful for finding defects in 3D printing, but we need to make sure our data stays private. The researchers created a new way to mix machine learning and privacy called DP-HD. It helps us understand how much noise is added to the data without losing accuracy. They tested it with real-world data from 3D printing and showed it can find defects really well while keeping the data safe. |
Keywords
* Artificial intelligence * Anomaly detection * Machine learning
