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-dlp and change directory to the sample directory you want to use.
$ git clone https://github.com/googleapis/python-dlp.git $ cd samples/snippetsInstall 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 3.6+.
$ virtualenv env $ source env/bin/activateInstall the dependencies needed to run the samples.
$ pip install -r requirements.txt
To run this sample:
$ python quickstart.pyTo run this sample:
$ python inspect_content.py
usage: inspect_content.py [-h] {string,table,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,table,file,gcs,datastore,bigquery}
Select how to submit content to the API.
string Inspect a string.
table Inspect a table.
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 exitTo run this sample:
$ python redact.py
usage: redact.py [-h] {info_types,all_text} ...
Sample app that uses the Data Loss Prevent API to redact the contents of an
image file.
positional arguments:
{info_types,all_text}
Select which content should be redacted.
info_types Redact specific infoTypes from an image.
all_text Redact all text from an image. The MIME type of the
file is inferred via the Python standard library's
mimetypes module.
optional arguments:
-h, --help show this help message and exitTo 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 exitTo 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 exitTo 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 exitTo 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 exitTo run this sample:
$ python deid.py
usage: deid.py [-h]
{deid_mask,deid_replace,deid_fpe,reid_fpe,deid_date_shift,replace_with_infotype}
...
Uses of the Data Loss Prevention API for deidentifying sensitive data.
positional arguments:
{deid_mask,deid_replace,deid_fpe,reid_fpe,deid_date_shift,replace_with_infotype}
Select how to submit content to the API.
deid_mask Deidentify sensitive data in a string by masking it
with a character.
deid_replace Deidentify sensitive data in a string by replacing it
with another string.
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.
replace_with_infotype
Deidentify sensitive data in a string by replacing it
with the info type of the data.
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.