The project achieved the ambition to enable practical privacy-preserving analytics on big data and bring scalable oblivious data analytics closer to reality with secure multiparty computation.
SODA created 6 demonstrators, integrating many of the results from the project. These demonstrate realistic use cases in MPC-based data analytics in healthcare and beyond. They also demonstrate the progress made on MPC performance, MPC-based machine learning algorithms, user studies and legal analysis.
SODA achieved the targets of the project objectives: enable MPC on big volumes / velocities / varieties of data, prevent leakage of undesired sensitive information, improve compliance with the EU GDPR, improve confidence of stakeholders in processing of personal information, and develop technical solutions that solve problems with real-world impact.
Aspects of MPC and demonstrators, legal and user studies were highlighted as part of dissemination. These events successfully reached out to audiences that are hard to reach via traditional channels.
Exploitation shows a number of highlights and opportunities. The improved FRESCO framework will be the basis for further business creation by Alexandra Institute. A TUE postdoc is launching a MPC start-up. Finally, Philips is prepared to apply MPC in its innovations, research studies and applications.
WP1 “Cryptographic Protocols” produced work of outstanding quality, which is recognized by the international research community: many of the results of this WP have been accepted for publications at the top-tier conferences for the field of cryptography.
WP2 “MPC-based analytics on Big Data” made contributions on methods and algorithms. From this, the work on secure linear algebra, secure comparison, and Conclave present more fundamental contributions, whereas the work on DNA matching and neural networks is more applied. Significant results include work on the Moore-Penrose pseudo-inverse, machine learning algorithms, and first results on streaming algorithms.
WP3 “Privacy: technical, legal, and user experience” delivered its main results. Progress on technical aspects relates to state of the art research performed and initial research in combining MPC with differential privacy. D3.1 focuses on the techniques being developed and provides a comprehensive overview on the most relevant legal challenges. Deliverable D3.5 provides an analysis of use-case-specific legal aspects and focuses on multi-party computation as a privacy-preserving tool. D3.4 on leakage control and differential privacy considers leakage of data from computations and how MPC and differential privacy can be combined to ensure privacy, and in some cases even improve performance. D3.2 provides the plan for the user studies and explain the idea behind the methodology. D3.3 presents the results of the user studies on data subjects and data scientists and how to approach aspects like technology, trust and consent.
WP4 “Demonstration” completed its challenge, proof-of-concept implementations and demonstrators. New releases of FRESCO incorporates many new MPC techniques and functionality, has improved maturity and performance, and is much easier to use by developers. The first release of MPyC is a third significant result that brings MPC closer to non-experts. A number of independent proof of concept and prototype implementations were created. Several real-world demonstrators show what has become possible with MPC-based data analytics today.
