# KART: Parameterization of Privacy Leakage Scenarios from Pre-trained Language Models

arXiv: https://arxiv.org/abs/2101.00036 · HTML: https://mlsanity.com/paper/2101.00036/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [Memorization in NLP Fine-tuning Methods](https://mlsanity.com/paper/2205.12506/) (2022)
- [Training Data Extraction From Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2305.16157/) (2023)
- [Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method](https://mlsanity.com/paper/2411.04388/) (2024)
- [Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper](https://mlsanity.com/paper/2404.17143/) (2024)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)

## Abstract (arXiv)

For the safe sharing pre-trained language models, no guidelines exist at present owing to the difficulty in estimating the upper bound of the risk of privacy leakage. One problem is that previous studies have assessed the risk for different real-world privacy leakage scenarios and attack methods, which reduces the portability of the findings. To tackle this problem, we represent complex real-world privacy leakage scenarios under a universal parameterization, textitKnowledge, Anonymization, Resource, and Target (KART). KART parameterization has two merits: (i) it clarifies the definition of privacy leakage in each experiment and (ii) it improves the comparability of the findings of risk assessments. We show that previous studies can be simply reviewed by parameterizing the scenarios with KART. We also demonstrate privacy risk assessments in different scenarios under the same attack method, which suggests that KART helps approximate the upper bound of risk under a specific attack or scenario. We believe that KART helps integrate past and future findings on privacy risk and will contribute to a standard for sharing language models.