PII Data Identification using Presidio Open Source ML Library

In today’s digital age, organizations deal with large amounts of sensitive data that includes PII data such as names, addresses, phone numbers, and email addresses. Protecting this data is critical to prevent identity theft and other types of fraud, and PII detection is a key step in the process. In this post, we will discuss Microsoft Presidio, an open-source ML-based library for detecting personally identifiable information (PII) in structured and unstructured data. Presidio supports the identification as well as anonymization of PII data in texts and images. However, in this post, we will be using the Presidio analyzer module to identify the PII data and will not be using the Presidio anonymizer that can help us to anonymize the personally identifiable information. Presidio library comes with many built-in PII identifiers and can detect:

1. Credit card number
2. Person names and place names
3. Locations like city, state, country, etc
4. Social security numbers
5. Financial data
6. Bitcoin wallets
7. US phone number
8. Email address and etc

In addition to the above list, it also facilitates users to create their own custom regex to identify the PII data that can not be identified with built-in PII recognizers. So, this library is highly customizable. Using machine learning algorithms, it can detect a wide range of PII data types, including names, addresses, phone numbers, and email addresses, among others. Presidio can be used to analyze text data in various formats, including plain text, HTML, and JSON, and can be integrated into a variety of applications and systems.

Key Features of Presidio:

1. PII Detection:

Presidio can recognize a wide range of PII (personally identifiable information) data with high accuracy. Under the hood, this library uses machine learning algorithms to detect PII data. We can train this library to detect new types of PII data if needed.

2. Customization:

Presidio library is highly customizable. It allows users to define their own PII data types using regex expressions. Users can also customize the detection rules and models used by the library as per their requirements.

3. Integration:

Presidio can be integrated easily with applications and systems such as web applications, mobile applications, and ETL/ELT data processing pipelines.

4. Scalability:

It is highly scalable and can handle large amounts of text data. We can use it to process real-time data also.

5. Performance:

It is designed to be fast and efficient, with low overhead and minimal impact on performance.

Presidio PII detection workflow

How to use presidio?

Here is a simple example of how to use the Presidio library to detect Personally Identifiable Information (PII) in Python:

from presidio_analyzer import PresidioAnalyzer

#Create an instance of the PresidioAnalyzer
analyzer = PresidioAnalyzer()

#The text to analyze
text = "My name is Steve Smith and my email is steve.smith@example.com. My phone number is 123-456-7890."

#Analyze the text and get the results
results = analyzer.analyze(text)

#Print the results
for result in results:
    print("Type:", result.entity_type)
    print("Value:", result.text)
    print("Score:", result.score)
    print()

The above code creates an object of the PresidioAnalyzer and then uses its analyze method to extract the PII data in the given text. It returns a list of Entity objects in which each Entity object represents a detected PII entity. The entity_type attribute of the Entity object represents the type of PII data. The text attribute represents the value of the PII data. Similarly, the score attribute represents the confidence level of the detection. Presidio provides many more features and options for customizing and fine-tuning the PII detection process. So, Presidio is a very powerful and flexible library that can be used for PII detection. Presidio is a great choice if you need to detect sensitive data in structured or unstructured text.

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