Cryptology ePrint Archive: Report 2019/1262

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

Shweta Agrawal and Rachit Garg and Nishant Kumar and Manoj Prabhakaran

Abstract: 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 notion is general enough to capture many real world scenarios that require controlled computation on encrypted data, such as is required for contact tracing in the wake of a pandemic. Our leakage and performance profile is similar to that of conventional searchable encryption systems, while the functionality we offer is significantly richer.

In more detail, the client specifies a query as a pair (Q, f) where Q is a filtering predicate which 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 evaluate the efficiency of our protocols in computations relevant to the Genome Wide Association Studies such as Minor Allele Frequency (MAF), Chi-square analysis and Hamming Distance.

Category / Keywords: cryptographic protocols / searchable symmetric encryption, secure cloud storage, encrypted search, functional encryption

Original Publication (in the same form): ESORICS 2020

Date: received 30 Oct 2019, last revised 31 Oct 2020

Contact author: nishant kr10 at gmail com, rachit0596 at gmail com, shweta a at gmail com, manojmp at gmail com

Available format(s): PDF | BibTeX Citation

Note: Full version of the conference version.

Version: 20201031:082631 (All versions of this report)

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