Machine learning with encryption to safeguard health and government data

      Services:
  • HARPOCRATES
  • MACHINE LEARNING
  • MULTIPARTY COMPUTATION
  • ENCRYPTION

Zentrix Lab is developing Federated Machine Learning Software

Use of available large volumes of user data is very limited due to the privacy concerns, which is the reason why the data are kept isolated in islands of system, not available for secondary use and processing. Furthermore, practice showed that in many scenarios data are unrightfully accessed and shared with third-party, and even, when the consent for the data processing exists, the learning models incorporate proxies that are inexact, biased and often unfair. 

HARPOCRATES, focuses on setting the foundations of digitally blind evaluation systems that will, by design, eliminate proxies such as geography, gender, race, and others and eventually have a tangible impact on building fairer, democratic and unbiased societies. To do so, we plan to design several practical cryptographic schemes (Functional Encryption and Hybrid Homomorphic Encryption) for analysing data in a privacy-preserving way.

Besides processing statistical data in a privacy-preserving way, we also aim to enable a richer, more balanced and comprehensive approach where data analytics and cryptography go hand in hand with a shift towards increased privacy.

In HARPOCRATES we will first show how to effectively combine cryptography with the principles of differential privacy to secure and privatise databases. Next, we will build privacy-preserving machine learning models able to classify encrypted data by performing high accuracy predictions directly on ciphertexts across federated data spaces. Finally, to demonstrate how these solutions respond to users’ needs, we will implement two real-world cross-border data sharing scenarios related to health data analysis for sleep medicine and threat intelligence for local authorities.

Project goals 

Availability of Big Data combined with advancements in Artificial Intelligence (AI) enable broad capabilities for both private and public actors. However, cross-organisation and cross-border data sharing in-line with GDPR is increasingly difficult, as collection of granular, multi-dimensional personal data meets improved capabilities to cross-link data sets. 

HARPOCRATES leverages novel cryptographic schemes to advance the capabilities of Privacy Preserving Machine Learning (PPML) and Federated Learning (FL), thus enabling decentralised training, validation, and prediction on encrypted data. Such privacy-preserving services and secure computation enable users to both benefit from cloud-based machine intelligence and maintain control over data. HARPOCRATES will enable digitally blind evaluation systems demonstrated in practical application scenarios, helping build fairer, democratic, and unbiased societies.

Zentrix role in the project 

Zentrix is developing federated machine learning platform to allow privacy preserving AI and processing of the datasets. Federated learning platform will enable training an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. 

Furthermore, Zentrix has an important role from the start of the project as a project lead for the dissemination, exploitation and standardization. The task will include participation in (standardization) working groups and delivery of new standards, with a focus on the consent and interoperability ontology for personal data (Electornic Data INterchange) EDI with enforced GDPR. 

 

Zentrix will be responsible for creating the Harpocrates ecosystem by leveraging existing ecosystems and communities, and thus minimizing the amount of work and resources which are required to contribute to strengthening the EU’s cybersecurity capacities and sovereignty in digital technologies. 

HARPOCRATES team and project duration

Harpocrates team comprises a consortium of 13 members, including universities, university hospitals, SMEs and regional government administration bodies from 9 different countries including United Kingdom, Finland, Sweden, Ireland, Serbia, Germany, Spain, Italy and France. Coordinator of the project is The University Of Westminster Lbg (UK). Project kickoff is in October and its planned to last until October 2025.

This project has received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101069535.

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  • HARPOCRATES
  • MACHINE LEARNING
  • DATA
  • DATA PRIVACY
  • FEDERATED LEARNING
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