Example 1: Deny-list based PII recognition

In this example, we will pass a short list of tokens which should be marked as PII if detected. First, let's define the tokens we want to treat as PII. In this case it would be a list of titles:

titles_list = [
    "Sir",
    "Ma'am",
    "Madam",
    "Mr.",
    "Mrs.",
    "Ms.",
    "Miss",
    "Dr.",
    "Professor",
]

Second, let's create a PatternRecognizer which would scan for those titles, by passing a deny_list:

from presidio_analyzer import PatternRecognizer

titles_recognizer = PatternRecognizer(supported_entity="TITLE", deny_list=titles_list)

At this point we can call our recognizer directly:

from presidio_analyzer import PatternRecognizer

text1 = "I suspect Professor Plum, in the Dining Room, with the candlestick"
result = titles_recognizer.analyze(text1, entities=["TITLE"])
print(f"Result:\n {result}")

Finally, let's add this new recognizer to the list of recognizers used by the Presidio AnalyzerEngine:

from presidio_analyzer import AnalyzerEngine

analyzer = AnalyzerEngine()
analyzer.registry.add_recognizer(titles_recognizer)

When initializing the AnalyzerEngine, Presidio loads all available recognizers, including the NlpEngine used to detect entities, and extract tokens, lemmas and other linguistic features.

Let's run the analyzer with the new recognizer in place:

results = analyzer.analyze(text=text1, language="en")

print("Results:")
print(results)

As expected, both the name "Plum" and the title were identified as PII:

print("Identified these PII entities:")
for result in results:
    print(f"- {text1[result.start:result.end]} as {result.entity_type}")