CoVault: Secure, Scalable Analytics of Personal Data

There is growing awareness that the analysis of personal data, such as individuals' mobility, financial, and health data, can provide significant benefits to society. However, liberal societies have so far refrained from such analytics, arguably due to the lack of secure analytics platforms that scale to billions of records while operating in a very strong threat model. We contend that one fundamental gap here is the lack of an architecture that can scale (actively-)secure multi-party computation (MPC) horizontally without weakening security. To bridge this gap, we present CoVault, an analytics platform that leverages server-aided MPC and trusted execution environments (TEEs) to colocate the MPC parties in a single datacenter without reducing security, and scales MPC horizontally to the datacenter's available resources. CoVault scales well empirically. For example, CoVault can scale the DualEx 2PC protocol [60] to perform epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using one core pair for every 30,000 people.