More and more data is being generated, and analyzing this data drives knowledge and value creation across society. Unlocking this potential requires sharing of (often personal) data between organizations, but this meets unwillingness from data subjects and data controllers alike. Hence, techniques that protect personal information for data access, processing, and analysis are needed. To address this, the SODA project will enable practical privacy-preserving analytics of information from multiple data assets using multi-party computation (MPC) techniques. For this data does not need to be shared, only made available for encrypted processing. The main technological challenge is to make MPC scale to big data, where we will achieve substantial performance improvements. We embed MPC into a comprehensive privacy approach, demonstrated in an ICT-14.b and a healthcare use case. Our first objective is to enable MPC 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. 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.

At first sight, privacy and strong identity seem inherently at odds. Indeed, if users are strongly identified during a transaction, then privacy is non-existent. Nevertheless, there exist mechanisms that can reconcile privacy and strong identity, either by trusting an online identity provider (IDP), or by using cryptographic mechanisms such as anonymous credentials. The former approach, made popular by technologies such as SAML, OpenID Connect, and Facebook Connect, has the disadvantage that the IDP forms a single point of failure in terms of privacy and security, because it can impersonate and track its users online. The latter approach has the disadvantage that users have to rely on trusted hardware such as smartcards to protect credentials from compromise and from illegitimate sharing. OLYMPUS will take a radically new approach offering the seamless user experience of online IDPs, but without their drawbacks. Namely, OLYMPUS will pioneer the concept of distributed oblivious identity management, where the role of the IDP is split over multiple authorities, so that no single authority can track or impersonate their users. By exploiting advanced techniques based on threshold cryptography, the OLYMPUS framework will let users maintain unlinkable identities with different service providers while using standard devices and a single password or biometric. By leveraging existing eID solutions to create a strong link to physical identities, and by integrating into existing frameworks to ease adoption by service providers, OLYMPUS will establish a secure and interoperable European identity management framework. Its practical feasibility and relevance will be demonstrated in two pilots. The first combines the framework with soft identity proofs to build a mobile driver license application that can be used for offline purchases of restricted goods. The second use case will leverage pseudonymous identification in the financial world to simplify online credit application