Artificial Intelligence
to advance humanity

We leverage our state-of-the-art Federated Learning Platform to make people’s lives better. The solutions will materially impact your Company’s P&L, save lives or improve your business results while preserving your customers’ privacy, complying with ESG standards and all applicable regulations.

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SCENARIOS unlocks the potential of data and AI while preserving privacy in a collaborative environment. Our platform changes the rules for organizations’ collaboration while ensuring compliance with all applicable regulation and ESG standards.

AI can massively impact business results and processes, redefining how decisions are made and transforming industries.

The potential of AI relies on data and the people who generate and own it. Therefore, privacy and security must be at the core of every AI solution.

At we have developed a privacy-enhancing Federated Learning platform that will have a massive impact on business results while preserving privacy and ensuring compliance with current and future regulation.
Keynote 2023

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Ritu Jyoti

Group Vice President, Worldwide Artificial Intelligence and Automation at IDC Group has emerged with a leading unique approach to Federated Learning which can overcome privacy and regulatory limitations to unlock new scenarios for collaboration between organizations that can lead to solving complex challenges with very impactful results.

Thomas Kalil

Former deputy Director of the White House Office of Science and Technology Policy platform brings additional benefits beyond data privacy, like security, as data is never shared, the attack surface is drastically reduced, as data can no longer be attacked in transit. has developed a two-layer privacy and security system. Data is never shared, and, on top of that, parameters are protected through integrated Privacy Enhancing Technologies (PETs) like Homomorphic Encryption, Secure Multiparty Computation or Differential Privacy.

TESTIMONIALS has created a unique platform that enables business, healthcare and other diverse users to realize the potential of Artificial Intelligence while maintaining user privacy. Preserving user privacy is not just an important moral and ethical imperative, it is a business necessity and will increasingly be required by governments around the world. provides this essential capability

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Doug Solomon

Former Chief Strategy officer at Apple

Sherpa is leading the way how artificial intelligence solutions will be built, preserving user privacy in all its forms

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Tom Gruber

Chief AI Strategy Officer at
Founder and CTO at SIRI is delivering the full potential of Artificial Intelligence by delivering a platform that ensures privacy, embraces the highest ethics, and exceeds current and expected governmental regulations

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Chris Shipley

Technology and strategy analyst, recognized as the most influential woman in Silicon Valley

Our mission is to make people lives better leveraging privacy-preserving AI as a fundamental ethical value.

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Xabi Uribe-Etxebarria

Founder & CEO at
MIT Technology Review TR35


Privacy-preserving platform
and technology by


Federated Learning allows AI model training with data owned by different parties, without sharing data. Only intelligence obtained by the models is shared, which allows to preserve privacy as all data remains on the site. The data never leaves the data owner's server, but it contributes to training and improving the accuracy of the global AI model.


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  • Higher risk of breaching privacy.
  • Not complaint with regulations.
  • Data control is lost once it leaves the server.
  • Large attack surface.



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  • Unlocks the potential of collaborative models without sharing private data.
  • Data privacy by design.
  • Regulatory compliance - data never leaves the server of the parties involved.
  • Lower risk of data breaches. The attack surface is reduced.
  • Transparency about how models are trained and how the data is used.


Federated Learning is not enough. Therefore has developed a platform that incorporates complementary Privacy-Enhancing Technologies (Differential Privacy, Secure Multi-party Computation or Homomorphic Encryption among others) to ensure robustness of the platform.'s platform has revolutionary potential for heavily regulated sectors like Healthcare or Financial Services, where privacy as well as regulatory compliance are essential. By adding complementary technologies to ensure privacy is maintained, unlocks new scenarios of development and collaboration between organizations.


Differential Privacy is a statistical technique to provide data aggregations, while avoiding the leakage of individual data records. This technique ensures that malicious agents intervening in the communication of local parameters cannot trace this information back to the data sources, adding an additional layer of data privacy.

Our platform applies Differential Privacy on top of everything which provives an empirical state-of-the-art trade-off between accuracy and privacy.


Secure Multiparty Computation (SMPC) is a cryptographic protocol that distributes the computation of data from different sources to ensure that no one can view other’s data, without the need to trust a third party.

Through SMPC it is ensured that your business’s sensitive data is secured, without restricting your ability to extract all the necessary information from data.


Homomorphic encryption allows to run computations on encrypted data without decrypting it. The resulting computations are also left encrypted and, when decrypted, the output is identical to the computation hadn't been ran on encrypted data.

This implies that data processing can be outsourced to a third party without needing to ensure that the third party secures the data correctly. Furthermore, without the decryption key, the original data can't be accessed.


With the use of cutting edge cryptographic techniques, the synchronization and identification of these datasets is possible while always protecting privacy and maintaining the performance of the trained models.

Private Set Intersection (PSI) determines the intersection of samples from all parties. It aligns them by comparing hashed/encrypted identifiers. Our cutting-edge technology, based on the n-grams separation, can overcome typos in the identifiers.


Zero-knowledge proof (ZKPS) is an encryption method that allows to verify specific information to another party without disclosing the information itself.

ZKPS is applied to defend against privacy inference attacks in Private Set Intersection.


Synthetic data generation is a technique to produce artificial datapoints from a real dataset, as a way of protecting data privacy.’s technology applies advanced synthetic data generation to avoid membership information leakage.

This unconventional solution, that allows to move away from standard methods, greatly reduces communication costs without degrading the accuracy of the predictive model.


About Us

and International Awards

See all our awards

B Intelligence Awards

Winner 2022 · Artificial Intelligence Excellence Award

Digital Talent

Winner 2022 · Digital Leader to Xabi Uribe-Etxebarria

CogX Winner 2021

Outstanding Contribution to Tech Regulation

Red Herring Europe

Winner 2022


Best in AI-As-A-Service Privacy-Preserving Platform



IMPACT & ESG has built a solution with ESG principles at its core and looking to help companies to make an ethical use of data, respect and preserve data privacy as well as contribute to reducing carbon footprint.

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DIGITAL ETHICS’s Federated Learning platform promotes an ethical use of Artificial Intelligence as it allows organizations to extract all benefits of data and achieve highly accurate AI models without sharing any data. Protecting customer’s privacy and preventing bias in model training.
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A large amount of energy and computational cost is required to train AI models in a centralised way. Federated Learning platform decentralizes data processing which cuts down energy consumption and contributes to reduce carbon footprint.

According to Cambridge Research, up to 70% in CO2 emissions can be reduced with Federated Learning vs centralized training.
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Compliance’s Federated Learning approach allows organizations to realise the full potential of data without sharing it.

With's plataform, all computations and model training occur in the data owner's environment. This massively reduces the risk of data breaches as well as the compliance burden. Data is never shared, therefore compliance with regulations like GDPR, HIPAA HITECH or CCPA is ensured.

This innovative approach unlocks the potential of data that is currently being underutilised due to existing regulation limiting data sharing but opens up new ways of collaboration between organizations with common problems that don't currently share data for competitive reasons.

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Privacy & Data Protection

Data privacy is a fundamental ethical value at

Our platform complies with all current regulations on Data Protection (GDPR) and is in line with the European Commission regulatory framework proposal on Artificial Intelligence.

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Information security is a top priority at

We believe that security must comply with quality standards and with all regulations in this regard. For this reason, we are certified in the ISO-27.001 data security standard and our platform has won the CogX 2021 awards for its Outstanding Contribution to Technology Regulation and has been a finalist as Best Solution for Privacy and Data Protection.


Maximize the value of data and AI with’s Privacy-Enhancing solutions

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