Summary of Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation, by Janvi Thakkar et al.
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
by Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach combining adversarial training and differentially private training is proposed to defend against simultaneous attacks on machine learning models. The method, building upon DP-Adv, outperforms existing state-of-the-art techniques in terms of performance while providing formal privacy guarantees through empirical validation using a membership inference attack. This work underscores the importance of exploring privacy guarantees in dynamic training paradigms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to keep machine learning models safe from hackers. They combined two techniques, adversarial training and differentially private training, to make their model more secure. The result is a system that is both good at predicting what will happen next and keeps personal information private. This work shows how important it is to think about privacy when training models. |
Keywords
* Artificial intelligence * Inference * Machine learning
