Summary of Bounding Reconstruction Attack Success Of Adversaries Without Data Priors, by Alexander Ziller et al.
Bounding Reconstruction Attack Success of Adversaries Without Data Priors
by Alexander Ziller, Anneliese Riess, Kristian Schwethelm, Tamara T. Mueller, Daniel Rueckert, Georgios Kaissis
First submitted to arxiv on: 20 Feb 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 Reconstruction attacks on machine learning models pose a significant risk to data leakage. Recent research has shown that an attacker can almost perfectly reconstruct training data samples from a trained model using its gradients, especially in specific contexts. To mitigate this threat, differential privacy (DP) is often employed during the training process of ML models. However, existing formal upper bounds on reconstruction attack success are based on worst-case assumptions that may not hold in realistic scenarios. This work provides novel formal upper bounds on reconstruction success under more practical adversarial settings against ML models trained with DP. The results demonstrate that these bounds can be used to make informed decisions about privacy parameters, taking into account the specific context and metrics used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning (ML) models are vulnerable to attacks that can reveal sensitive information. When training an ML model, a bad actor could potentially recreate the original data from which it was trained. To protect against this type of attack, researchers use something called differential privacy (DP). So far, experts have only been able to provide upper bounds on how well these attacks would succeed under unrealistic scenarios. In this study, scientists developed new formulas that estimate the success rate of such attacks in more realistic situations. These formulas can help us make better choices about how much privacy we want to prioritize. |
Keywords
* Artificial intelligence * Machine learning
