A Practical Model for Collaborative Databases: Securely Mixing, Searching and Computing

We introduce the notion of a Functionally Encrypted Datastore which collects data anonymously from multiple data-owners, stores it encrypted on an untrusted server, and allows untrusted clients to make select-and-compute queries on the collected data. Little coordination and no communication is required among the data-owners or the clients. Our security and performance profile is similar to that of conventional searchable encryption systems, while the functionality we offer is significantly richer. The client specifies a query as a pair (Q,f) where Q is a filtering predicate that selects some subset of the dataset and f is a function on some computable values associated with the selected data. We provide efficient protocols for various functionalities of practical relevance. We demonstrate the utility, efficiency, and scalability of our protocols via extensive experimentation. In particular, we use our protocols to model computations relevant to the Genome-Wide Association Studies such as Minor Allele Frequency (MAF), Chi-square analysis and Hamming Distance. Our experiments show our system is capable of accumulating data from 100,000+ data owners in a few hundred seconds, while also allowing clients to query the data in a few seconds with a few MB of total communication.

Updates

• [July ‘20] Accepted for publication in European Symposium on Research in Computer Security (ESORICS), 2020.

Links:

1. eprint