Innovate across organizations without risking privacy or IP

Privacy is a fundamental human right. Data ecosystems that aim to improve the lives of millions of people only function if privacy is preserved and IP is protected.

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Why Apheris?

Apheris helps you to build data ecosystems in which privacy is not only a core value, but undisputed priority. All participants can rest assured that sensitive data always stays private and in full control of the data owner, while having the flexibility to get deep insights from the data.

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Innovate on privacy-sensitive data

Train machine learning models at scale across multiple organizations, all while protecting full data privacy

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Manage privacy-enhancing technologies

Safely and universally manage privacy-enhancing technologies with granular policy controls across companies

Ensure auditability and data governance

Be audit-ready, fulfil highest ethical standards and ensure responsible AI across organizations

How it works

We understand that there is no universal privacy-preserving data sharing mechanism: Apheris layers and orchestrates various privacy-enhancing technologies such as Federated Learning, Differential Privacy and Synthetic Data with the highest level of precision, to allow for deep access to federated data and for trusted collaboration.

Privacy-enhancing technologies, access controls and additional security layers are automatically enforced and abstracted to the user.

The complexity of a collaborative data ecosystem grows as more participants enter the network. As a fully managed service, Apheris takes care of all privacy settings, as well as legal and contracting across organizations.

By streamlining auditing and governance for compliance and regulatory efforts, all participants have more time and resources to focus on value generating tasks.

Federated architecture

Collaborate on data and AI without having to move, share or centralize data.

Isolated & confidential environments

Apheris offers highly secure computing environments that provide remote access to federated data for approved users.

Privacy-enhancing technologies and IP protection

Through the modular application of PETs, data utility is preserved and the IP of all assets stays protected.

Full control

Privacy parameters are fully controlled by the Data Provider. Ecosystem participants can interact with and analyze data according to the parameters set by the Provider, ensuring data sovereignty and full control of the data remains with the owner.

Flexibility and interoperability

Fine-grained access and algorithm controls ensure protection of data while offering first-of-its kind flexibility to run full data science and AI workflows.

Compliance and Accreditation

The entire environment of Apheris is built on best practices and principles:

Industry-leading standards and policies:

  • Federated Trusted Research Environments
  • Federated Data Networks
  • Gaia-X
  • GDPR

International Organization for Standardization (ISO):

  • ISO 27001



The Three Adoption Stages of Privacy-enhancing Technologies (And Why We Are Stuck on Level Two)

PETs are massively changing how we operate, and how we have to think about the data and AI landscape. Introducing such a game-changer into large enterprises has to be done with the highest precision, and a lot of foresight.


7 Myths About Federated Learning

In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.


How to Choose the Best Federated Learning Platform in 2022

How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.

Interested in collaborative data ecosystems for your industry?

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