MORE and more data is being generated. Analyzing this data drives knowledge and value creation across society.
BUT to unlock this potential requires sharing of often personal data between organizations, but this meets unwillingness from data subjects and data controllers alike.
SO there is a need of techniques that protect personal information for data access, processing, and analysis.
The SODA project will enable practical privacy-preserving analytics of information from multiple data assets using multi-party computation techniques. This means data does not need to be shared, only made available for encrypted processing.
HEALTHCARE will be our first use case.
- SODA presented at the Data Science Summit Eindhoven (DSCE) 2018.
- We published deliverables General MPC Framework: Development of Novel General Purpose MPC Protocols
- Deliverable Application Oriented MPC Protocols is available.
Our FIRST objective is to enable Multi Party Computation (MPC) techniques for big data applications by scaling the performance. We follow a use case-driven approach, combining expertise from the domains of MPC and data analytics.
Our SECOND objective is to combine these improvements with a multidisciplinary approach towards privacy. By enabling differential privacy in the MPC setting aggregated results will not leak individual personal data. Legal analysis performed in a feedback loop with technical development will ensure improved compliance with EU data privacy regulation.
Our USER STUDIES performed in a feedback loop with our consent control component will make data subjects more confident to have their data processed with our techniques.
Our FINAL objective is to validate our approach, by applying our results in a medical demonstrator originating from Philips practice and in a use case arising from the ICT-14.b data experimentation incubators. The techniques will be subjected to public hacking challenges. The technical innovations will be released as open-source improvements to the FRESCO MPC framework.
The SODA Model
The project supports cohesive and secure use of personal (health) data.
Step 1: Health data
There is a huge potential in using the massive amounts of data that healthcare entities and patients have collected over the past decades. At the same time, personal health information needs to be protected.
Step 2: Security techniques
The research team uses two basic security techniques to anonymise data: Secure multiparty computation and differential privacy.
Step 3: Big data research and analytics
Enables companies, authorities and researchers to perform data analytics on private (big) data without compromising on security.