Keep It Private

This is the repository for the project Keep it Private: Unsupervised Privatization of Online Text

How to Install

• The requirements will need to be installed.

$ scripts/setup.sh

This will create a conda environment named kip. You will need to download the checkpoints into the models directory. The script will prompt you to run the correct commands.

Keep It Private Overview

Keep it Private performs authorship transfer by performing authorship transfer using a seq2seq model that was adversarially fine-tuned via reinforcement learning using a set of rewards (Privacy, Sense, and Soundness metrics)

Command template

$ conda activate kip
$ python src/generate.py --input_data_path ${INPUT_DATA_PATH} \
      --output_path ${OUTPUT_PATH} \
      --model_path models \
      --model_name_to_use dipper-large \
      --model_start_file ${BIN_FILE}  \ 
      --token_max_length 256 \

Example

python src/generate.py --input_data_path {JSONFILE} \
      --output_path {OUTPUT_FILE} \
      --model_path models \
      --model_name_to_use dipper-large \
      --model_start_file models/dipper_v130.bin \
      --token_max_length 256 \

Parameters

1. input_data_path: path to the query documents to be privatized
2. output_path: file to save the privatized documents
3. model_start_file: path to trained KiP model
4. model_name_to_use: path to pre-trained base model
5. token_max_length: max cutoff length of output
6. random_seed: initialize all random seed to this value