Availability of large volumes of data combined with tailored data analysis present a unique opportunity for organizations to adapt and finetune their services according to individual needs. Having shown remarkable results in analyzing data, Machine Learning (ML) models attracted global interest and are applied in a wide range of application areas including medical diagnostics, pattern recognition, and threat intelligence. However, it might come with privacy losses. Furthermore, practice showed that systems using ML models incorporate proxies that are often inexact, biased and unfair. To address these challenges HARPOCRATES focused on setting the foundations of digitally blind analysis and evaluation systems that 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, HARPOCRATES designed several cryptographic schemes: Functional Encryption (FE) and Hybrid Homomorphic Encryption (HHE) for analyzing data in a privacy-preserving way using Machine Learning (ML). Besides processing data in a privacy-preserving way, HARPOCRATES outputs also enabled a richer, more balanced and comprehensive approach where data analytics and cryptography go hand in hand with a shift towards increased privacy and security. HARPOCRATES first, showed how to effectively combine cryptography with Differential Privacy (DP) to secure and privatise databases. Second, the project built Privacy Preserving Machine Learning (PPML) models that are able to classify encrypted data performing high accuracy predictions directly on ciphertexts across federated data spaces. Finally, HARPOCRATES demonstrated how these solutions respond to users’ needs implementing two real-world cross-border data sharing scenarios related to health data analysis for sleep medicine (Sleep Medicine demonstrator) and threat identification for local authorities (Threat Intelligence demonstrator).
HARPOCRATES addressed the following objectives:
Objective 1. Designing efficient function-hiding FE schemes
Objective 2. Combining FE and DP for private encrypted databases
Objective 3. Designing a practical multi-client HHE scheme
Objective 4. Building a PPML framework by combining FE and HHE
Objective 5. Creating Byzantine-robust FL scheme with data privacy guarantees
Objective 6. Real-world case studies and contribution to Open Science and Reproducible Research
Objective 7. Contributing to Scalable Automated GDPR Compliance.
