PII Application

About

This application identifies likely PII (personally identifiable information) in a dataset. To use, download the .exe installer from the latest release and follow the in-app directions.

This tool is current listed as an alpha release because it is still being tested on IPA PII-containing field datasets.

How does it work?

There are a series of rules that are applied to a dataset's column to identify if a given column is a PII. Such rules are:

• If column name or label match with any word of the list of restricted words ( ex 'name', 'surname', 'ssn', etc; check restricted_words.py). The match could be strict or fuzzy. Check find_piis_based_on_column_name() in PII_data_processory.py.
• If entries in a given column have a specific format (at the moment checking phone number format and date format, we can expand to gps, national identifiers, etc). Check find_piis_based_on_column_format() in PII_data_processory.py.
• If all entries in a given column are sufficiently sparse (almost all unique). Ideal to identify open ended questions. Check find_piis_based_on_sparse_entries() in PII_data_processory.py.
• If columns with locations have any location with population under 20,000. Check find_piis_based_on_locations_population() in PII_data_processory.py.

Importantly, this is an arbitrary defined list of conditions, and for sure can be improved. Very open to feedback!

Once the PIIs are identified, users have the opportunity to say what they would like to do with those columns. Options are: drop column, encode column or keep column. According to those instructions, a new de-identified dataset is created. Also, the system outputs a log .txt file and a .csv file that maps the new and encoded values.

Finding PII in unstructured text

The repo has code written to identify PII in text, and replace the PIIs for a 'xxxxxx' string. So, rather than flagging a whole column and dropping/encoding it, they user might prefer to replace the PII by this string and keep everything else. The code searches for PII based on classic common names of people and cities. This functionality is finished but super slow at the moment, so it is currently not enabled.

Files included

Main files

• app_frontend.py: App GUI script using tkinter.
• PII_data_processor.py: App backend, it reads data files, identifies PIIs and creates new de-identified data files.
• find_piis_in_unstructed_text.py: Script used by PII_data_processor to particularly detect piis in unstructured text

Other utility files

• restricted_words.py: Script to get restricted words for PII identification
• constant_strings.py: Declares strings used across app.
• query_google_answer_boxes.py: Script to query locations and populations
• dist folder: Contains .exe file for execution
• hook-spacy.py: Dependency file needed when creating .exe

How to run

python app_frontend.py

Remember to install dependencies mentioned in requirements.txt.

Distribution

To create executable app

pyinstaller --windowed --icon=app_icon.ico --add-data="app_icon.ico;." --add-data="ipa_logo.jpg;." --add-data="anonymize_script_template_v2.do;." --additional-hooks-dir=. --hiddenimport srsly.msgpack.util --noconfirm app_frontend.py

To create windows application installer

Compile create_installer.iss using Inno Setup Compiler Reference: https://www.youtube.com/watch?v=RrpvNvklmFA https://www.youtube.com/watch?v=DTQ-atboQiI&t=135s

Credit

IPA's RT-DEG teams.

J-PAL: stata_PII_scan. 2020. https://github.com/J-PAL/stata_PII_scan

J-PAL: PII-Scan. 2017. https://github.com/J-PAL/PII-Scan

Licensing

The PII script is MIT Licensed.