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Privacy-first AI

  • Ensures privacy & control
  • Leverage your valuable data
  • Enable collaboration with third parties
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Our vision

We help organizations unlock the value of their data

High-quality data is incredibly valuable. That’s why it’s not a good idea to simply give away your data. With DetaBord, you can capitalize on the value of your data, integrate and develop AI on your sources, collaborate with internal and external partners, and even monetize your data – all while keeping your data protected and maintaining complete control.

Your data. Your control.

When it comes to enabling AI on your data, we take a different approach. Instead of transferring your data, we bring AI models to the data source for training and scoring. That means the data stays in your environment and control. These measures provide a secure and controlled manner to use your data for state-of-the-art AI (and even share it with interested parties) – without neglecting data protection. 

Roundhouse compatibility

  • Widely compatible and integrates well with other tools and into your existing workflows
  • Backward compatible with datasets and technical systems (e.g. legacy data, SQL, Excel)
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Access to sandbox & third party tools

  • Future-ready with our integrated sandbox and connectors to AI modeling software solutions to develop more specific models for your datasets
  • Securely test and develop models like ChatGPT in the sandbox

Privacy-first AI

Ensures privacy & control

The data always remains in control of the data owner. DetaBord offers fully customizable access controls and policy mechanisms. Privacy-enhancing technologies like differential privacy and encryption provide verifiable data protection, bulletproof transactional logs guarantee complete transparency and auditability.

 

Leverage your valuable data

Improve internal efficiency and gather important information for your organization based on your unique data. Continue on the insights provided by generative AI with the development of specific models with your data by trusted AI developers and data scientists. 

 

Enable collaboration with third parties

Enhance collaborative research and development. With DetaBord, you can securely connect internal units or external partners. You can also monetize your data without compromising personal or commercial liability.

 

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Blog & Resources

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Data Sharing in Healthcare

Due to worldwide digitalization we are experiencing a growing flow of data within hospitals and other medical institutions that is generated, stored and used for the treatment of patients.

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TÜV Certificate for DetaBord

DetaBord is a software platform for privacy-preserving machine learning, which enables external analysts to access confidential internal data and to process them anonymously with the help of differential privacy.

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FAQ

Because DetaBord 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. DetaBord sets the highest standards for the protection of your data.
DetaBord sets the highest standards in data protection and data security. We have a legal opinion that certifies that DetaBord is suitable for compliance with data protection legislation. Contact us at hello@detabord.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 DetaBord 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. DetaBord ensures strict data protection guarantees with precisely calibrated noise in order to achieve the best utility-privacy trade off.

DetaBord implements all data protection methods carefully and securely and tests this security continuously according to the latest scientific criteria. DetaBord is encrypted end-to-end. The software to control the platform is certified by TÜV Austria as a “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, our parent company. DetaBord offers a tested and verifiable solution for data protection in machine learning and SQL analytics.
DetaBord ensures the secure implementation of this budget. The level of protection is always transparent to data owners. At the same time, DetaBord 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 DetaBord solutions, i.e. for a decentralized application of differential privacy, e.g. with federated learning. Contact us: hello@detabord.com

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