PII Identification and Removal

The purpose of the personal identifiable information (PII) de-identification tool is to help scrub sensitive data out of datasets. The following is the list of sensitive data types that are currently supported by the tool:

  • Name

  • Email Address

  • Physical Address

  • Phone Numbers

  • IP Address

  • Credit/Debit Card Numbers

  • US Social Security Numbers

  • Dates

The tool utilizes Dask to parallelize tasks and hence it can be used to scale up to terabytes of data easily. Although Dask can be deployed on various distributed compute environments such as HPC clusters, Kubernetes and other cloud offerings such as AWS EKS, Google cloud etc, the current implementation only supports Dask on HPC clusters that use SLURM as the resource manager.

Reading documents and de-identifying

Imagine we have a “books” dataset stored in the following structure:

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books_dataset/ books_00.jsonl books_01.jsonl books_02.jsonl

You could read, de-identify the dataset, and write it to an output directory using the following approach

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from nemo_curator.datasets import DocumentDataset from nemo_curator.utils.distributed_utils import read_data, write_to_disk, get_client from nemo_curator.utils.file_utils import get_batched_files from nemo_curator.modules.modify import Modify from nemo_curator.modifiers.pii_modifier import PiiModifierBatched modifier = PiiModifierBatched( language="en", supported_entities=["PERSON", "EMAIL_ADDRESS"], anonymize_action="replace", batch_size=1000, device="gpu") for file_names in get_batched_files( "book_dataset, "output_directory", "jsonl", 32 ): source_data = read_data(file_names, file_type="jsonl", backend='pandas', add_filename=True) dataset = DocumentDataset(source_data) print(f"Dataset has{source_data.npartitions}partitions") modify = Modify(modifier, batched=True) modified_dataset = modify(dataset) write_to_disk(modified_dataset.df, "output_directory", write_to_filename=True, output_type="jsonl" )

Let’s walk through this code line by line.

  • modifier = PiiModifierBatched creates an instance of PiiModifierBatched class that is responsible for PII de-identification

  • for file_names in get_batched_files retrieves a batch of 32 documents from the book_dataset

  • source_data = read_data(file_names, file_type="jsonl", backend='pandas', add_filename=True) reads the data from all the files using Dask using Pandas as the backend. The add_filename argument ensures that the output files have the same filename as the input files.

  • dataset = DocumentDataset(source_data) creates an instance of DocumentDataset using the batch files. DocumentDataset is the standard format for text datasets in NeMo Curator.

  • modify = Modify(modifier, batched=True) creates an instance of the Modify class. This class can take any modifier as an argument

  • modified_dataset = modify(dataset) modifies the data in the dataset by performing the PII de-identification based upon the passed parameters.

  • write_to_disk(modified_dataset.df .... writes the de-identified documents to disk.

The PII redaction module can also be invoked via script/find_pii_and_deidentify.py script which provides a CLI based interface. To see a complete list of options supported by the script just execute

python nemo_curator/scripts/find_pii_and_deidentify.py

To launch the script from within a SLURM environment, the script examples/slurm/start-slurm.sh can be modified and used.

Resuming from Interruptions

It can be helpful to track which documents in a dataset have already been processed so that long curation jobs can be resumed if they are interrupted. NeMo Curator provides a utility for easily tracking which dataset shards have already been processed. A call to get_batched_files will return an iterator over the files that have yet to be processed by a modifier such as PiiModifierBatched When you re-run the code example provided above, NeMo Curator ensures that only unprocessed files are processed by the PII module.

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