Summary of Inference Attacks: a Taxonomy, Survey, and Promising Directions, by Feng Wu et al.
Inference Attacks: A Taxonomy, Survey, and Promising Directions
by Feng Wu, Lei Cui, Shaowen Yao, Shui Yu
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper addresses concerns about data privacy in machine learning as a service (MLaaS) scenarios. Inference attacks can breach privacy by analyzing outputs of target models to infer characteristics of undisclosed training sets. For instance, inferring whether data has AIDS-like characteristics. The rapid development of ML has stimulated research on inference attacks. A systematic discussion of these attacks and countermeasures is urgent. This survey provides a comprehensive analysis of inference attacks and countermeasures in MLaaS based on taxonomy and latest research. It proposes the 3MP taxonomy to normalize naming systems, analyzes pros and cons of each attack type, and points out promising directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning can be powerful, but it also raises concerns about data privacy. Some attacks can guess what kind of training data a model was trained on just by looking at its outputs. This is called an inference attack. It’s like trying to figure out what someone’s personality traits are based on how they answer questions. The more machine learning grows, the more these kinds of attacks will be studied. Right now, there isn’t a clear way to group and understand all these different types of attacks. This paper tries to fix that by creating a system to categorize inference attacks and finding ways to stop them. |
Keywords
» Artificial intelligence » Inference » Machine learning
