CoVault: A Secure Analytics Platform
arxiv(2022)
摘要
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.
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