Discovering health disparities: Designing a secure multiparty architecture for social health research

Social determinants such as a person’s race, level of education, and income can be responsible for their health outcomes. Consequently, we see that discrimination along the social spectrum results in health disparities. In an effort to close the gaps in healthcare systems, these determinants have been heavily researched. Open questions remain...

A New Standard: Redesigning Autonomous Vehicle Communication Using Multi-Party Computation

Around the world millions of people get injured due to traffic accidents. Autonomous vehicles are expected to significantly reduce these numbers. To increase safety, autonomous vehicle communication can be used. Current vehicle communication networks called VANETs have security and privacy protection problems and the vehicle industry is...

Making private GPS data available to policy makers: Investigating the feasibility of multi-party computation for smart mobility

With recent advances in performance and complexity, multi-party computation, a privacy-preserving technology which allows for joint processing of hidden input data, has lately been found to be applicable in a number of use cases. Despite existing implementations for secure data aggregation, substantial adoptions of the technology remain limited...

Secure multi-party computation for supply chain collaboration

Despite evidence that collaborating in the supply chain can reduce inefficiency and result in mutual gain, parties do not wish to collaborate if they have to share their private proprietary information. The main reason for their privacy concern is that the party does not want to lose their competitive advantage by giving away company secrets....