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Inspecting text for sensitive data
Detect and classify sensitive information contained within text strings and text files.
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Inspecting structured text for sensitive data
Detect and classify sensitive information contained within structured text strings.
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Inspecting images for sensitive data
Detect and classify sensitive information contained within images.
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Inspecting storage and databases for sensitive data
Detect and classify sensitive information contained within content stored in Google Cloud Storage, Datastore, and BigQuery.
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Creating inspection templates
Use templates to create and persist inspect job configuration information.
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Listing built-in infoType detectors
Programmatically retrieve a list of all currently supported built-in infoType detectors.
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Creating custom infoType detectors
Create your own information type detectors to use for inspection and redaction.
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Creating a regular custom dictionary detector
Create your own regular dictionary custom detectors to use for inspection and redaction.
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Creating a stored custom dictionary detector
Create large dictionary custom detectors to inspect storage repositories.
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Creating a custom regex detector
Create your own regular expression detectors to use for inspection and redaction.
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Modifying infoType detectors to refine scan results
Refine the scan results that Cloud DLP returns by modifying the detection mechanism of a given infoType detector.
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Customizing match likelihood
Use hotword context rules to extend your custom infoType detectors.
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Measuring re-identification and disclosure risk
Compute the likelihood that de-identified data will be re-identified, the risk of sensitive attribute disclosure, and the risk of dataset membership disclosure.
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Computing k-anonymity for a dataset
Learn how to compute the _k_-anonymity metric, a property of a dataset that indicates the re-identifiability of its records.
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Computing l-diversity for a dataset
Learn how to compute the _l_-diversity metric, an extension of _k_-anonymity that measures the diversity of sensitive values for each column in which they occur.
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Computing k-map for a dataset
Learn how to compute the _k_-map metric, which is very similar to _k_-anonymity except that it assumes that the attacker most likely doesn't know who is in the dataset.
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Computing δ-presence for a dataset
Learn how to compute the _δ_-presence metric, which quantifies the probability that an individual belongs to an analyzed dataset.
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Visualizing re-identification risk using Google Data Studio
Measure the k-anonymity of a dataset using Cloud Data Loss Prevention (DLP) and visualize it in Data Studio to determine the re-identifiability of the data.
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Computing numerical and categorical statistics
Compute numerical and categorical numerical statistics for individual columns in BigQuery tables.
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Sending Cloud DLP scan results to Data Catalog
Instruct Cloud DLP to send results directly to Data Catalog.
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Sending Cloud DLP scan results to Security Command Center
Instruct Cloud DLP to send results directly to Security Command Center.
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Analyzing and reporting on Cloud DLP scan findings
Generate reports and run rich SQL analytics based on Cloud DLP scan findings.