Summary of The Fundamental Limits Of Least-privilege Learning, by Theresa Stadler et al.
The Fundamental Limits of Least-Privilege Learning
by Theresa Stadler, Bogdan Kulynych, Michael C. Gastpar, Nicolas Papernot, Carmela Troncoso
First submitted to arxiv on: 19 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 In this paper, researchers aim to formalize the concept of least-privilege learning, which involves finding feature representations useful for a specific task while preventing inference of sensitive information unrelated to that task. To achieve this goal, they develop a framework that characterizes the feasibility of least-privilege learning and prove that there is a fundamental trade-off between representation utility and leakage beyond the intended task. The study shows that it’s not possible to learn representations with high utility for the intended task while preventing inference of any attribute other than the task label itself, regardless of the learning technique or model architecture used. The findings are validated through experiments on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure machine learning models don’t accidentally reveal private information they’re not supposed to know. Imagine you want a model to recognize pictures of dogs and cats without revealing any personal details. This is called least-privilege learning, and it’s a big deal. The problem is that nobody has figured out how to make this work in practice. This paper takes the first step by explaining what least-privilege learning really means and whether it’s even possible. The answer is no – there’s a trade-off between how well the model does its job and how much private information it leaks. |
Keywords
* Artificial intelligence * Inference * Machine learning
