https://github.com/joisino/prism
Code for "Making Translators Privacy-aware on the User's Side" (TMLR 2024)
https://github.com/joisino/prism
llm machine-learning privacy translation
Last synced: 8 months ago
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Code for "Making Translators Privacy-aware on the User's Side" (TMLR 2024)
- Host: GitHub
- URL: https://github.com/joisino/prism
- Owner: joisino
- License: mit
- Created: 2024-06-10T05:30:47.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-10T05:33:46.000Z (about 2 years ago)
- Last Synced: 2025-03-21T11:31:25.663Z (over 1 year ago)
- Topics: llm, machine-learning, privacy, translation
- Language: Python
- Homepage: https://arxiv.org/abs/2312.04068
- Size: 478 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Making Translators Privacy-aware on the User’s Side (TMLR 2024)
[](https://arxiv.org/abs/2312.04068)
We propose PRISM to enable users of machine translation systems to preserve the privacy of data on their own initiative.
Paper: https://arxiv.org/abs/2312.04068
## ✨ Summary
▲ **Overview of PRISM**: PRISM converts the input sentence into a privacy-less sentence and sends it to the machine translation system. PRISM then converts the translated sentence back into the original sentence.
## 💿 Preparation
Install [Poetry ](https://python-poetry.org/) and run the following command:
```bash
$ poetry install
$ poetry run bash prepare.sh
```
Set an OpenAI API key in `.env`.
## 🧪 Evaluation
```bash
$ poetry run python eval.py --method prismstar --translator chatgpt
$ poetry run python eval.py --method prismr --translator chatgpt
$ poetry run python eval.py --method nodecode --translator chatgpt
$ poetry run python eval.py --method pup --translator chatgpt
```
Please refer to the help command for further options.
```
$ poetry run python eval.py -h
usage: eval.py [-h] [--lang LANG] [--basedir BASEDIR] [--rates RATES] [--method {pup,prismr,prismstar,nodecode}] [--translator {chatgpt,t5,t5-gpu}]
optional arguments:
-h, --help show this help message and exit
--lang LANG
--basedir BASEDIR
--rates RATES
--method {pup,prismr,prismstar,nodecode}
--translator {chatgpt,t5,t5-gpu}
```
### Results
▲ **Results.** PRISM* strikes an excellent balance between privacy and translation quality.
Please refer to the paper for more details.
## ⛏️ How to Build a Dictionary by Yourself
Run the following command to extract candidate words from the corpus. It uses `load_mctest()` for the corpus. You can replace it with your own corpus. In general, it is recommended to use the same or similar corpus as the one used in the evaluation.
```bash
$ poetry run python extract_all_words.py
```
Then, run the following command to build a dictionary. It build a dictiory based on wmt14 dataset (i.e., a public news corpus).
```bash
$ poetry run python build_dict.py 1 -1 --target French
$ poetry run merge_cand_words.py cand_words_French_1000
```
Bulding the entire dictionary may take a long time. You can build each part separately (in separate machines) and merge them.
```bash
$ poetry run python build_dict.py 1 100 --target French
$ poetry run python build_dict.py 100 200 --target French
$ poetry run python build_dict.py 200 300 --target French
...
$ poetry run merge_cand_words.py cand_words_French_1000
```
## 🖋️ Citation
```
@article{sato2024making,
author = {Ryoma Sato},
title = {Making Translators Privacy-aware on the User’s Side},
journal = {Transactions on Machine Learning Research},
year = {2024},
}
```