AI & Data Platform with
built-in privacy

With new privacy technologies like Differential Privacy and
Federated Learning we help you securely analyze sensitive data

For Data Officers

DetaBord is available as private cloud SaaS or on-premise, installed “in front of” your data storage solution. The data itself does not have to be changed for use in DetaBord. The integration effort is minimal. Basically, all data sources can be connected. DetaBord works with structured data from SQL and No-SQL databases, file systems and cloud storage as well as with binary data such as images or measurement data.

You can attach a wide variety of different data sources

Formats

Connectors

You can talk to DetaBord with

By Data Scientists
for Data Scientists

DetaBord was developed by data scientists for data scientists.

In addition to the core functionality of computing models on protected data, DetaBord offers features for model versioning and tracking of your experiments based on the popular open-source framework mlflow.

You can work with all your favorite frameworks, directly with our free Python SDK.

Additionally, use our built-in web application to keep track of all your projects, experiments and runs and collaborate with your colleagues in research partners with DetaBord’s complete machine learning and SQL development platform.

Our system integrates well with all major frameworks

DetaBord SDK

The DetaBord Software Development Kit is a free Python library with which data scientists can work with DetaBord easily and with known tools.

See the example to the right of working with the DetaBord SDK.

Documentation

If you want to know more, you can find more information in the DetaBord Documentation (coming soon). Until then, you can join our waitlist below.

# import dq0sdk
from dq0sdk.core import Project, Experiment

# create a project with name 'model_1'. Automatically creates the 'model_1' directory and changes to this directory.
project = Project(name='model_1')

# Create experiment for project
experiment = Experiment(project=project, name='experiment_1')

# Train an model
run = experiment.train()

# wait for completion
run.wait_for_completion(verbose=True)

# get training results
print(run.get_results())

# get the latest model
model = project.get_latest_model()

# check DQ0 privacy clearance
if model.predict_allowed:
    # call predict
    run = model.predict(np.array([1, 2, 3]))

Privacy Technologies

DetaBord is both technically tested by TÜV Austria and legally rated as a data protection compliant solution. Contact us for more information.

If sensitive data is to be used from other departments or external groups, the methods of anonymization or synthetization are often chosen to protect the sensitive information. Unfortunately, anonymizing or synthesizing the data is not secure. There are numerous studies that have shown that secret information can also be obtained from supposedly completely anonymous data records.

Rocher, L., Hendrickx, J.M. & de Montjoye, Y. Estimating the success of re-identifications in incomplete datasets using generative models. Nat Commun 10, 3069 (2019). 

Machine learning, in particular, poses great challenges to data security, since certain models can be used to track the information obtained about individual data records. DetaBord, therefore, implements a robust security concept based on the principle of Differential Privacy in order to enable AI modeling for statements about the entirety of the data and at the same time to protect individual data records.

With DetaBord, data science analyzes can be carried out to retrieve general information about the data records used, without endangering individual data points. A valuable general statement such as “if the properties a and b are present, the therapy under consideration has a healing probability of p” can be formulated, statements that are prohibited by data protection law such as “due to the presence of properties a and b, patient x must be present in the data set” are not possible.

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