This directory contains samples for Google Data Loss Prevention. Google Data Loss Prevention provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams.
This sample requires you to have authentication setup. Refer to the Authentication Getting Started Guide for instructions on setting up credentials for applications.
Clone python-docs-samples and change directory to the sample directory you want to use.
$ git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
Install pip and virtualenv if you do not already have them. You may want to refer to the Python Development Environment Setup Guide for Google Cloud Platform for instructions.
Create a virtualenv. Samples are compatible with Python 2.7 and 3.4+.
$ virtualenv env $ source env/bin/activateInstall the dependencies needed to run the samples.
$ pip install -r requirements.txt
For running *_test.py files, install test dependencies
$ pip install -r requirements-test.txt $ pytest inspect_content_test.py
** _test.py files are demo wrappers and make API calls. You may get rate limited for making high number of requests. *
To run this sample:
$ python quickstart.py <project-id>
To run this sample:
$ python inspect_content.py
usage: inspect_content.py [-h] {string,file,gcs,datastore,bigquery} ...
Sample app that uses the Data Loss Prevention API to inspect a string, a local
file or a file on Google Cloud Storage.
positional arguments:
{string,file,gcs,datastore,bigquery}
Select how to submit content to the API.
string Inspect a string.
file Inspect a local file.
gcs Inspect files on Google Cloud Storage.
datastore Inspect files on Google Datastore.
bigquery Inspect files on Google BigQuery.
optional arguments:
-h, --help show this help message and exit
To run this sample:
$ python redact.py
usage: redact.py [-h] [--project PROJECT] [--info_types INFO_TYPES]
[--min_likelihood {LIKELIHOOD_UNSPECIFIED,VERY_UNLIKELY,UNLIKELY,POSSIBLE,LIKELY,VERY_LIKELY}]
[--mime_type MIME_TYPE]
filename output_filename
Sample app that uses the Data Loss Prevent API to redact the contents of a
string or an image file.
positional arguments:
filename The path to the file to inspect.
output_filename The path to which the redacted image will be written.
optional arguments:
-h, --help show this help message and exit
--project PROJECT The Google Cloud project id to use as a parent
resource.
--info_types INFO_TYPES
Strings representing info types to look for. A full
list of info categories and types is available from
the API. Examples include "FIRST_NAME", "LAST_NAME",
"EMAIL_ADDRESS". If unspecified, the three above
examples will be used.
--min_likelihood {LIKELIHOOD_UNSPECIFIED,VERY_UNLIKELY,UNLIKELY,POSSIBLE,LIKELY,VERY_LIKELY}
A string representing the minimum likelihood threshold
that constitutes a match.
--mime_type MIME_TYPE
The MIME type of the file. If not specified, the type
is inferred via the Python standard library's
mimetypes module.
To run this sample:
$ python metadata.py
usage: metadata.py [-h] [--language_code LANGUAGE_CODE] [--filter FILTER]
Sample app that queries the Data Loss Prevention API for supported categories
and info types.
optional arguments:
-h, --help show this help message and exit
--language_code LANGUAGE_CODE
The BCP-47 language code to use, e.g. 'en-US'.
--filter FILTER An optional filter to only return info types supported
by certain parts of the API. Defaults to
"supported_by=INSPECT".
To run this sample:
$ python jobs.py
usage: jobs.py [-h] {list,delete} ...
Sample app to list and delete DLP jobs using the Data Loss Prevent API.
positional arguments:
{list,delete} Select how to submit content to the API.
list List Data Loss Prevention API jobs corresponding to a given
filter.
delete Delete results of a Data Loss Prevention API job.
optional arguments:
-h, --help show this help message and exit
To run this sample:
$ python templates.py
usage: templates.py [-h] {create,list,delete} ...
Sample app that sets up Data Loss Prevention API inspect templates.
positional arguments:
{create,list,delete} Select which action to perform.
create Create a template.
list List all templates.
delete Delete a template.
optional arguments:
-h, --help show this help message and exit
To run this sample:
$ python triggers.py
usage: triggers.py [-h] {create,list,delete} ...
Sample app that sets up Data Loss Prevention API automation triggers.
positional arguments:
{create,list,delete} Select which action to perform.
create Create a trigger.
list List all triggers.
delete Delete a trigger.
optional arguments:
-h, --help show this help message and exit
To run this sample:
$ python risk.py
usage: risk.py [-h] {numerical,categorical,k_anonymity,l_diversity,k_map} ...
Sample app that uses the Data Loss Prevent API to perform risk anaylsis.
positional arguments:
{numerical,categorical,k_anonymity,l_diversity,k_map}
Select how to submit content to the API.
numerical
categorical
k_anonymity Computes the k-anonymity of a column set in a Google
BigQuerytable.
l_diversity Computes the l-diversity of a column set in a Google
BigQuerytable.
k_map Computes the k-map risk estimation of a column set in
a GoogleBigQuery table.
optional arguments:
-h, --help show this help message and exit
To run this sample:
$ python deid.py
usage: deid.py [-h] {deid_mask,deid_fpe,reid_fpe,deid_date_shift} ...
Uses of the Data Loss Prevention API for deidentifying sensitive data.
positional arguments:
{deid_mask,deid_fpe,reid_fpe,deid_date_shift}
Select how to submit content to the API.
deid_mask Deidentify sensitive data in a string by masking it
with a character.
deid_fpe Deidentify sensitive data in a string using Format
Preserving Encryption (FPE).
reid_fpe Reidentify sensitive data in a string using Format
Preserving Encryption (FPE).
deid_date_shift Deidentify dates in a CSV file by pseudorandomly
shifting them.
optional arguments:
-h, --help show this help message and exitThis sample uses the Google Cloud Client Library for Python. You can read the documentation for more details on API usage and use GitHub to browse the source and report issues.