Towards Scalable Secure Analytics of Personal Data

Awareness is growing that the statistical analysis of personal data, such as individuals’ mobility, financial, and health data, could be of significant benefit to society. However, liberal societies have refrained from such analytics, arguably due to the lack of trusted analytics platforms that scale to billions of records while reliably preventing the leakage and misuse of personal data. In this talk, I will sketch CoVault, a prototype analytics platform that leverages server-aided secure multi-party computation (MPC) and trusted execution environments (TEEs) to colocate the MPC parties in a single datacenter without reducing security. CoVault can scale MPC horizontally to the datacenter’s available resources. For example, CoVault can scale the DualEx 2PC protocol 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.