Summary of Naturally Private Recommendations with Determinantal Point Processes, by Jack Fitzsimons et al.
Naturally Private Recommendations with Determinantal Point Processes
by Jack Fitzsimons, Agustín Freitas Pasqualini, Robert Pisarczyk, Dmitrii Usynin
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 and statistical analysis methods can be modified to ensure differential privacy, but some models are inherently private or require fewer changes. This paper discusses Determinantal Point Processes (DPPs), a type of dispersion model that balances recommendations based on popularity and diversity. The authors introduce DPPs, derive the modifications needed for epsilon-Differential Privacy, analyze their sensitivity, and propose efficient alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be made private by introducing randomization, but some are naturally private or require fewer changes. This paper looks at Determinantal Point Processes (DPPs), which balance recommendations based on popularity and diversity. It explains how DPPs work, makes them private, and proposes efficient versions. |
Keywords
» Artificial intelligence » Machine learning
