---
language:
- fa
license: other
multilingual: false
pretty_name: encrypted_legal_dataset
task_categories:
- other
task_ids: []
source_datasets: []
dataset_info:
  features:
  - name: AnonymizedJudge_text
    dtype: string
  - name: AnonymizedJSS_text
    dtype: string
  - name: UniqueTables
    dtype: string
  - name: UniqueItemArray
    dtype: string
  - name: JSSType
    dtype: string
  - name: othersdoc
    dtype: string
  splits:
  - name: train
    num_bytes: 449947800
    num_examples: 19256
  download_size: 450863360
  dataset_size: 449947800
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# ara_v7

**ara_v7** is a dataset where the `text` column has been encrypted with **AES-GCM (AES-256)** 
to preserve privacy while still allowing distribution.

## Dataset Description

- **Columns**:
  - `score`: floating-point metadata value
  - `text`: Base64-encoded string containing AES-GCM encrypted text
- **Encryption**:
  - AES-256 in GCM mode


## Usage

You can load the dataset using the 🤗 Datasets library:

```python
import base64
from cryptography.hazmat.primitives.ciphers.aead import AESGCM
from datasets import load_dataset

# 🔑 Replace this with the Base64 key provided securely
key_b64 = "PASTE-YOUR-KEY-HERE"
key = base64.b64decode(key_b64)
aesgcm = AESGCM(key)

def decrypt(token: str) -> str:
    data = base64.b64decode(token.encode())
    nonce, ciphertext = data[:12], data[12:]
    return aesgcm.decrypt(nonce, ciphertext, None).decode()

# Load dataset
dataset = load_dataset("QomSSLab/ara_v7")

# Decrypt rows
dataset = dataset.map(lambda x: {key: decrypt(x[key]) for key in ['AnonymizedJudge_text','AnonymizedJSS_text', 'UniqueTables', 'UniqueItemArray','JSSType','othersdoc']})
```