Unparalleled Insights
from Sensitive Data

Privacy-first data platform for ethical AI and trustworthy analytics

DQ0 is a machine learning and SQL analytics platform with built-in data privacy


Benefit from most valuable insights. Use all the data. Improve business processes and gather important information for decision making.


DQ0 mathematically guarantees privacy. Stay in control of your data and avoid leaks and fines by never revealing any secret information.


Let the analysis come to the data! Do away with error-prone anonymization or synthetization and analyze the data right where it is.

Develop Trustworthy AI

  • Built-in principles of ethical AI
  • Secure architecture, TÜV trusted application
  • Fully GDPR compliant
  • Based on newest technologies like differential privacy and federated learning

Create secure collaborative data rooms

  • Overcome boundaries across your business
  • Connect different business units
  • Break data silos
  • Enhance collaborative research and development

Create Data Marketplaces

  • Share your data insights with anyone while keeping data protected
  • Increase your margins by making data reuse possible
  • Expand your business with data marketplaces
  • Offer privacy-preserving data services to third parties

Frequently Asked Questions

Because DQ0 relies on the mathematically secure concept of Differential Privacy and at the same time offers a well thought-out platform for data science and SQL analytics. DQ0 sets the highest standards for the protection of your data.
DQ0 sets the highest standards in data protection and data security. We have a legal opinion, which certifies that DQ0 is suitable for compliance with data protection legislation. Contact us at dq0@gradient0.com for more information.
Differential privacy makes data protection measurable. With every data query something is revealed about the data (otherwise the query itself would be meaningless). Questions to supposedly completely anonymized (or pseudonymized, or synthesized) data also reveal information about the original data. And usually much more than desired. Differential Privacy sets a mathematically defined standard for this unwanted information retrieval, and DQ0 guarantees that it remains as low as possible.
Because the process of producing the synthetic data must also be safe. Synthetic data promise the same properties as the original data set without disclosing sensitive information. Unfortunately, this promise is unsustainable. As of the current state of research, this is simply not possible, not even in the foreseeable future. Solutions that promise otherwise should be treated with great caution.
Yes. But if this data previously contained personal information and was anonymized, pseudonymized or synthesized, it is not really anonymous, but allows conclusions to be drawn about secret data. There are innumerable cases that show, for example, how certain persons could be attributed to seemingly completely anonymized data (membership disclosure). "This is [..] why Cynthia Dwork […] likes to say "anonymised data isn’t" – either it isn’t really anonymous or so much of it has been removed that it is no longer data." From "The Ethical Algorithm" by Michael Kearns and Aaron Roth. (link)
Data protection is always associated with costs. To protect a person's personal data, noise is added. And that in turn affects the data analysis results. DQ0 ensures strict data protection guarantees with precisely calibrated noise in order to achieve the best utility-privacy trade off.
DQ0 implements all data protection methods carefully and securely and tests this security continuously according to the latest scientific criteria. DQ0 is encrypted end-to-end. The software to control the platform is certified by TÜV Austria as "Trusted Application". (link)
And yet few do. Differential Privacy is a mathematical concept, not a solution. The reliable implementation of these methods requires the interaction of experienced experts from mathematics, data science and computer science as they come together at Gradient Zero. DQ0 offers a tested and verifiable solution for data protection in machine learning and SQL analytics.
DQ0 ensures the secure implementation of this budget. The level of protection is always transparent to data owners. At the same time, DQ0 ensures maximum usability of the data, even for sophisticated SQL queries or data science jobs.
Differential privacy can also be applied directly. Ask our experts about individual DQ0 solutions, i.e. for a decentralized application of differential privacy, e.g. with federated learning. Contact us: dq0@gradient0.com.
DQ stands for data quarantine, the secure enclave for your data. We borrowed the Zero from our company Gradient Zero.