Summary of P3ls: Partial Least Squares Under Privacy Preservation, by Du Nguyen Duy et al.
P3LS: Partial Least Squares under Privacy Preservation
by Du Nguyen Duy, Ramin Nikzad-Langerodi
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 The paper proposes Privacy-Preserving Partial Least Squares (P3LS), a federated learning technique that enables cross-organizational data integration and process modeling while ensuring privacy guarantees. P3LS employs a singular value decomposition-based algorithm, removable masks generated by a trusted authority, and randomization to protect contributed data. The technique is demonstrated on a hypothetical value chain of three parties, showing improved prediction performance on key performance indicators. Numerical equivalence with traditional Partial Least Squares (PLS) models is also shown, along with a thorough privacy analysis. Moreover, the paper proposes a mechanism for quantifying contributor relevance and contribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in making decisions in companies that work together to make things. Companies have valuable information they don’t want to share because it might be private. The authors created a way to combine this information, called P3LS, which keeps the information safe while still helping the companies make better decisions. They tested their method on some pretend company data and showed it works well. This could help companies become more sustainable and profitable. |
Keywords
* Artificial intelligence * Federated learning
