Summary of Ft-privacyscore: Personalized Privacy Scoring Service For Machine Learning Participation, by Yuechun Gu et al.
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
by Yuechun Gu, Jiajie He, Keke Chen
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 the pressing concern of training data privacy in AI modeling. While methods like differentiated private learning allow data contributors to quantify acceptable privacy loss, model utility is often significantly damaged. The authors propose a novel approach to controlled data access, where authorized model builders work in a restricted environment to access sensitive data, preserving data utility with reduced risk of data leak. However, this method lacks a quantitative measure for individual data contributors to assess their privacy risk before participating in a machine learning task. The proposed solution is the demo prototype FT-PrivacyScore, which efficiently and quantitatively estimates the privacy risk of participating in a model fine-tuning task. The authors provide a demo source code available at https://github.com/RhincodonE/demo_privacy_scoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps keep your personal data safe when artificial intelligence is trained on sensitive information. Right now, there are ways to protect this data, like letting authorized people work in a special area where they can access the sensitive information while keeping it private. But there’s no way for individuals to know how much their own privacy is being risked before sharing their data. The researchers created a new tool called FT-PrivacyScore that helps estimate how much personal data might be shared when fine-tuning AI models. This tool could help people make better decisions about whether they want to share their data or not. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning
