Description du projet
Analyse des données et cryptographie pour protéger la vie privée
Afin de répondre aux besoins des clients, les organisations s’appuient sur de larges volumes de données des utilisateurs combinés à des analyses statistiques personnalisées afin d’adapter leurs services en conséquence. Des modèles d’apprentissage automatique sont utilisés dans les applications. Toutefois, de telles améliorations des services et la personnalisation basée sur l’analyse des données des utilisateurs augmentent le risque de perte de confidentialité. En outre, les systèmes utilisant de tels modèles intègrent souvent des serveurs mandataires inexacts, partiaux et injustes. Le projet HARPOCRATES, financé par l’UE, posera les bases pour des systèmes d’évaluation conçus pour éliminer les serveurs mandataires et dont les données ne seront pas révélées. Le projet prévoit de concevoir plusieurs schémas cryptographiques pratiques (cryptage fonctionnel et cryptage homomorphique hybride) pour analyser les données de manière à préserver la vie privée et permettre une approche globale où l’analyse des données et la cryptographie sont associées à une meilleure protection de la vie privée.
Objectif
Availability of large volumes of user data combined with tailored statistical analysis present a unique opportunity for organizations across the spectrum to adapt and finetune their services according to individual needs. Having shown remarkable results in analyzing user data, machine learning models attracted global adulation and are applied in a plethora of applications including medical diagnostics, pattern recognition, and threat intelligence. However, such service improvements and personalization based on user data analysis come at the heavy cost of privacy loss. Furthermore, practice showed that systems that use such models incorporate proxies that are often inexact, biased and often unfair.
In HARPOCRATES, we focus 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 analyzing 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.
Champ scientifique
- medical and health sciencesclinical medicinepsychiatrysleep disorders
- natural sciencescomputer and information sciencesdata science
- natural sciencescomputer and information sciencesdatabases
- natural sciencescomputer and information sciencescomputer securitycryptography
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinateur
33100 Tampere
Finlande