Cryptology ePrint Archive: Report 2021/1661

Waldo: A Private Time-Series Database from Function Secret Sharing

Emma Dauterman and Mayank Rathee and Raluca Ada Popa and Ion Stoica

Abstract: Applications today rely on cloud databases for storing and querying time-series data. While outsourcing storage is convenient, this data is often sensitive, making data breaches a serious concern. We present Waldo, a time-series database with rich functionality and strong security guarantees: Waldo supports multi-predicate filtering, protects data contents as well as query filter values and search access patterns, and provides malicious security in the 3-party honest-majority setting. In contrast, prior systems such as Timecrypt and Zeph have limited functionality and security: (1) these systems can only filter on time, and (2) they reveal the queried time interval to the server. Oblivious RAM (ORAM) and generic multiparty computation (MPC) are natural choices for eliminating leakage from prior work, but both of these are prohibitively expensive in our setting due to the number of roundtrips and bandwidth overhead, respectively. To minimize both, Waldo builds on top of function secret sharing, enabling Waldo to evaluate predicates non-interactively. We develop new techniques for applying function secret sharing to the encrypted database setting where there are malicious servers, secret inputs, and chained predicates. With 32-core machines, Waldo runs a query with 8 range predicates over $2^{18}$ records in 3.03s, compared to 12.88s for an MPC baseline and 16.56s for an ORAM baseline. Compared to Waldo, the MPC baseline uses 9 − 82× more bandwidth between servers (for different numbers of records), while the ORAM baseline uses 20 − 152× more bandwidth between the client and server(s) (for different numbers of predicates).

Category / Keywords: cryptographic protocols / encrypted databases

Original Publication (in the same form): IEEE Security & Privacy 2022

Date: received 17 Dec 2021

Contact author: edauterman at berkeley edu, mayankr at berkeley edu

Available format(s): PDF | BibTeX Citation

Version: 20211220:135548 (All versions of this report)

Short URL: ia.cr/2021/1661


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