Create safe data

The first and only APIs to enable you to balance, anonymize, and share your data. With privacy guarantees.

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Discover. Transform. Share.

Create an anonymized or synthetic dataset to safely work with data while preserving privacy.
Example code
Web console
hello_gretel.py
from gretel_client import get_cloud_client

client = get_cloud_client("api", GRETEL_API_KEY)
my_data = load_from_csv("my_sensitive_data.csv")
labeled_data = client.detect_entities(my_data)

print(labeled_data)
$~ python hello_gretel.py
{
 "project_record_count": 11515,
 "total_entity_count": 8,
 "data": {
   "entities": [
     {
       "entity": "location",
       "count": 76151.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 32748
     },
     {
       "entity": "latitude",
       "count": 23030.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 10678
     },
     {
       "entity": "longitude",
       "count": 23030.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 10905
     },
     {
       "entity": "md5",
       "count": 17353.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 14377
     },
     {
       "entity": "url",
       "count": 17353.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 109
     },
     {
       "entity": "uuid",
       "count": 15885.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 13385
     },
     {
       "entity": "datetime",
       "count": 11515.0,
       "last_seen": "2020-07-06T20:55:54Z",
       "approx_card": 119
     },
     {
       "entity": "person_name",
       "count": 1013.0,
       "last_seen": "2020-07-06T20:55:42Z",
       "approx_card": 49
     }
   ]
 }
}
hello_gretel.py
import pandas as pd

from gretel_synthetics.batch import DataFrameBatch

config = {
   "epochs": 15,
   "vocab_size": 20000,
   "dp": True,  # enable differential privacy,
   "field_delimiter": ",",
   "checkpoint_dir": "my_models"
}

df = pd.read_csv("my_sensitive_data.csv")

gretel = DataFrameBatch(df=df, config=config)
gretel.create_training_data()
gretel.train_all_batches()  # build AI models
gretel.generate_all_batch_lines()

synthetic_data_df = gretel.batches_to_df()
synthetic_data_df.head()
transform.py
from gretel_client.transformers import DataPath, DataTransformPipeline, FakeConstantConfig, RedactWithLabelConfig

SEED = 8675309
RECORD = {
   "name": "Clark Kent",
   "address": "344 Clinton Street, Apartment 3D, Metropolis",
   "mobile": "800-579-7866",
   "email": "[email protected]"
}

name = FakeConstantConfig(seed=SEED, fake_method="name")
address = FakeConstantConfig(seed=SEED, fake_method="address")
auto_redact = RedactWithLabelConfig(labels=["phone_number", "email_address"])

paths = [
   DataPath(input="name", xforms=[name]),
   DataPath(input="address", xforms=[address]),
   DataPath(input="*", xforms=[auto_redact])
]

pipe = DataTransformPipeline(paths)
print(pipe.transform_record(RECORD))
$ python transform.py
{
   "name": "Dr. Julia Evans, MD",
   "address": "1061 Autumn Station Apt. 934 Lake Tiffany, FL 56915",
   "mobile": "PHONE_NUMBER",
   "email": "EMAIL_ADDRESS"
}
transform.py
import requests

from gretel_client import project_from_uri
from gretel_client.transformers import DataPath, DataTransformPipeline, FakeConstantConfig

# We don't want to write sensitive data into our analysis store
faker = FakeConstantConfig(
   seed=12345,
   labels=["email_address", "person_name", "phone_number", "date_of_birth", "address"])
path = [DataPath(input="*", xforms=[faker])]
transformer = DataTransformPipeline(path)

project = project_from_uri(GRETEL_URI)

# stream labeled data from the Gretel API, sanitize and
# send it to the analysis store
while True:
   for record in project.iter_records():
       safe_record = transformer.transform_record(record)
       requests.post("https://analysis.store:9200/customers/_doc", json=safe_record)

Use cases

Gretel can help speed up development workflows and enable your team to move quickly, efficiently, and safely.

Improve limited datasets with synthetic data

Use synthetic data to augment data sources, improve accuracy, and reduce bias in machine learning models.

Create synthetic data with privacy guarantees

Create and share realistic synthetic data freely across teams and organizations with differential privacy guarantees.

Power testing environments with anonymized data

Create real-time transformations to power development, test, and staging pipelines with all of the dynamism of real data.

Workflow agnostic, data forward

Gretel offers you all the tools you need to augment and improve your current data processing workflows.

Data Catalog

  • Stream data via our API or SDKs
  • Explore records, labels and fields
  • Create and share datasets

Transformation

  • Simple APIs and clear docs
  • Encrypt or replace sensitive data
  • Anonymize data in real-time

Synthetics

  • AI-based and open source
  • Differential privacy enabled
  • Generate unlimited data

Data transformation made easy

Gretel gives you the tools to make customer data useful and safe for developers and companies.

Synthetics

Generate a synthetic dataset for training ML models and safely sharing data with privacy guaranteed.

Explore Synthetics

Transformations

De-identify, anomymize, and mask datasets to share internally or with the public.

Explore Transformers

Thank you!

We are so excited to be launching soon! We will send you an email when our public beta is ready.
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