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About DQ0

DQ0 is a platform for secure data storage and processing. With DQ0, data records that contain sensitive information such as personal data or business secrets can be stored in a secure enclave. At the same time, DQ0 ensures that this data can be used for data science analysis and machine learning.

DQ0 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 method of anonymization is often chosen to protect the sensitive information. Unfortunately, anonymization 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). https://www.nature.com/articles/s41467-019-10933-3

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. DQ0 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 DQ0, 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.

More information about Differential Privacy:

• Differential Privacy: A Primer for a Non-technical Audience
• The Algorithmic Foundations of Differential Privacy

For Data Officers

DQ0 is available as

• a managed, private cloud Software as a Service (Saas) solution, or
• an on-premise version, installed direclty "in front" of your data

You can attach a wide variety of different data sources.

With DQ0, all operations on the data are performed within the secure data quarantine, within your secure environment. The DQ0 platform offers various options for performing the calculations for easy integration into your IT.

As a data officer, with DQ0 you always have full control over the use of your data. DQ0 offers its own interface for the administration of the data access and for the administration of users and models. This interface is available as a command line tool and as a web application.

All operations performed on your data are logged by DQ0 (in a data protection-compliant manner) so that you can carry out a complete audit at any time.

DQ0 provides data owners with a completely transparent view of all activity on your data. Data owners can adjust the privacy settings managed by DQ0 per dataset and grant or revoke access to data users at any time.

Additionally, external access to your DQ0 instance is completely end-to-end encrypted for maximum information security.

For Data Scientists

DQ0 was developed by data scientists for data scientists. In addition to the functionality of computing models on protected data, DQ0 offers convenient options for model versioning and for parameterization and tracking of your experiments.

You can talk to DQ0 with

• the freely available DQ0 CLI command line program (available for Linux, Mac and Windows) with built-in end-to-end encryption
• the integrated web application
• the DQ0 SDK, directly from your Jupyter development environment

DQ0 SDK

The DQ0 Software Development Kit is a Python library with which data scientists can work with DQ0 easily and with known tools. The SDK is freely available: https://github.com/gradientzero/dq0-sdk

Example of working with the DQ0 SDK:

# 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]))

There is also a quickstart notebook with more detailed examples: https://github.com/gradientzero/dq0-sdk/blob/master/dq0sdk/cli/DQ0SDK-Quickstart.ipynb