Concepts in Microsoft Presidio

High-level concepts

Concept	Definition	Learn More
Entity	An entity is a span of text that can be used to directly identify an individual. For example, a phone number, email address, or social security number. In Presidio, an entity is represented by a RecognizerResult object.	Analyzer concepts
Context	Context is defined as the surrounding text of an entity. Context can be used to provide additional information about the entity which can be used to improve the detection accuracy.	Analyzer concepts
Recognizer	A recognizer is an object that is responsible for detecting entities in text. Recognizers can be rule-based, machine learning-based, or a combination of both. The Presidio Analyzer orchestrates multiple recognizers to detect PII entities in text. The main objects in Presidio that implement PII detection logic are the EntityRecognizer and PatternRecognizer.	Analyzer concepts
Analyzer	The Presidio AnalyzerEngine is responsible for orchestrating the PII detection using various recognizers.	Analyzer concepts
Predefined recognizer	A recognizer that already exists in Presidio	Predefined recognizers
Custom recognizer	A recognizer that is added by the user	Adding recognizers
ad-hoc recognizer	A recognizer that is added to the request itself, rather than to the list of recognizers loaded within Presidio	ad-hoc recognizers
Deny list	A list of terms that should always be identified as PII	denylist tutorial
Allow list	A list of terms that should not be identified as PII	allowlist tutorial

Main objects in Presidio

Concept	Definition	Learn More
EntityRecognizer	An EntityRecognizer is an object in Presidio that is responsible for detecting entities in text. An entity recognizer can be rule-based, a machine learning model, or a combination of both.	Analyzer concepts
RecognizerResult	A RecognizerResult holds the type and span of a PII entity.	Analyzer concepts
RecognizerRegistry	The RecognizerRegistry is a class in Presidio that is responsible for holding the various recognizers used by the AnalyzerEngine.	link
NlpEngine	The NlpEngine is an interface that defines the methods for processing text. Presidio provides several implementations of the NlpEngine, such as SpacyNlpEngine, TransformersNlpEngine, and StanzaNlpEngine.	Analyzer concepts
AnalyzerEngine	The AnalyzerEngine is the main class in Presidio that is responsible for orchestrating the PII detection in text. It uses an NlpEngine to process the text and a RecognizerRegistry to hold the different recognizers.	Analyzer concepts
BatchAnalyzerEngine	The BatchAnalyzerEngine is a class in Presidio that is responsible for detecting PII entities in a batch of texts. It uses the AnalyzerEngine to process each text in the batch.	Batch processing sample
AnonymizerEngine	The AnonymizerEngine is the main class in Presidio that is responsible for anonymizing PII entities in text. It uses the results from the AnalyzerEngine to perform the anonymization.	Anonymizer concepts
DeanonymizerEngine	The DeanonymizerEngine is a class in Presidio that is responsible for deanonymizing text that has been anonymized by the AnonymizerEngine, given that the operation is reversible (e.g. encryption).	Anonymizer concepts
Operator	An Operator is an object in Presidio that is responsible for performing the anonymization operation on a PII entity. Presidio provides several built-in operators, such as Replace, Redact, and Encrypt, and allows users to create custom operators.	Anonymizer concepts
BatchAnonymizerEngine	The BatchAnonymizerEngine is a class in Presidio that is responsible for anonymizing PII entities in a batch of texts. It uses the AnonymizerEngine to perform the anonymization on each text in the batch.	Sample
ImageRedactorEngine	The ImageRedactorEngine is a class in Presidio that is responsible for redacting PII entities in images. It leverages the AnalyzerEngine to detect PII entities in the text extracted from the images.	Image redaction docs
StructuredEngine	The StructuredEngine is a class in Presidio that is responsible for detecting PII entities in structured data. It uses the AnalyzerEngine to detect PII entities in the text fields of the structured data.	Image redaction docs

Evaluation concepts

Concept	Definition	Learn More
Precision	Precision is a metric that measures the proportion of true positive results among the positive results. In the context of PII detection, precision measures the proportion of correctly identified PII entities among all the entities identified by the system.	Evaluation docs
Recall	Recall is a metric that measures the proportion of true positive results among the actual positive results. In the context of PII detection, recall measures the proportion of correctly identified PII entities among all the PII entities present in the text.	Evaluation docs