CoVault: A Secure Analytics Platform

TL;DR

CoVault is a secure analytics platform that combines MPC and TEE to enable privacy-preserving large-scale data analysis, achieving high scalability and significantly improved query performance over existing MPC solutions.

Contribution

We introduce CoVault, a novel platform integrating MPC and TEE to securely and efficiently analyze personal data at large scale.

Findings

CoVault processes data for epidemic analytics of 80M people.

It achieves 7 to over 100 times faster query processing than state-of-the-art MPC.

CoVault scales to many data sources and big data volumes.

Abstract

Analytics on personal data, such as individuals' mobility, financial, and health data can be of significant benefit to society. Such data is already collected by smartphones, apps and services today, but liberal societies have so far refrained from making it available for large-scale analytics. Arguably, this is due at least in part to the lack of an analytics platform that can secure data through transparent, technical means (ideally with decentralized trust), enforce source policies, handle millions of distinct data sources, and run queries on billions of records with acceptable query latencies. To bridge this gap, we present an analytics platform called CoVault which combines secure multi-party computation (MPC) with trusted execution environment (TEE)-based delegation of trust to be able execute approved queries on encrypted data contributed by individuals within a datacenter to achieve the above properties. We show that CoVault scales well despite the high cost of MPC. For example, CoVault can process data relevant to epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using a core pair for every 20,000 people. Compared to a state-of-the-art MPC-based platform, CoVault can process queries between 7 to over 100 times faster, as well as scale to many sources and big data.