Summary of Indiscriminate Disruption Of Conditional Inference on Multivariate Gaussians, by William N. Caballero et al.
Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians
by William N. Caballero, Matthew LaRosa, Alexander Fisher, Vahid Tarokh
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Applications (stat.AP)
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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 addresses a gap in adversarial machine learning (AML) by exploring inference attacks on multivariate Gaussian models. The authors consider a self-interested attacker seeking to corrupt evidentiary variables while appearing plausible, as determined by the density of the corrupted evidence. Two attack settings are examined: white-box, where the attacker knows the underlying model, and grey-box, where this knowledge is incomplete. The attacks are shown to reduce to quadratic and stochastic quadratic programs, with structural properties derived for solution methods. Three examples demonstrate the attacks’ applicability in real estate evaluation, interest rate estimation, and signals processing. These applications highlight how uncertainty and structure affect attacker behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it harder for computers to make good decisions by adding fake information that makes sense. The authors want to see if they can trick a computer into thinking something is true when it’s not. They look at two ways the attacker could do this: knowing the rules of the game or having some knowledge but not all. They show how this works in simple cases and use three examples to demonstrate how this could be used in real estate, interest rates, and sound processing. |
Keywords
* Artificial intelligence * Inference * Machine learning
