Summary of Attack-aware Noise Calibration For Differential Privacy, by Bogdan Kulynych et al.
Attack-Aware Noise Calibration for Differential Privacy
by Bogdan Kulynych, Juan Felipe Gomez, Georgios Kaissis, Flavio du Pin Calmon, Carmela Troncoso
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 a novel approach to calibrating the noise scale in differential privacy (DP) mechanisms for machine learning models. The traditional method involves choosing a privacy budget ε and translating it to attack risks such as accuracy, sensitivity, and specificity of inference attacks. However, this can lead to overly conservative risk assessments and unnecessarily low utility. Instead, the authors suggest directly calibrating the noise scale to a desired attack risk level, bypassing the step of choosing ε. This approach significantly decreases the noise scale, leading to increased utility at the same level of privacy. The authors demonstrate that calibrating noise to attack sensitivity/specificity rather than ε when training privacy-preserving ML models substantially improves model accuracy for the same risk level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make sure machine learning models don’t leak private information. Right now, people add noise to these models to keep their data safe. The problem is that it’s hard to know just the right amount of noise to add. Some methods try to figure this out by thinking about how bad the consequences would be if someone tried to get the private information back. But this can make things too safe, and actually make the model worse. Instead, the authors suggest doing things differently and adding less noise while still keeping the data safe. This makes the model better at doing its job. |
Keywords
* Artificial intelligence * Inference * Machine learning
