Processing Analytical Queries over Encrypted Data

By Stephen Tu, M. Frans Kaashoek, Samuel Madden, Nickolai Zeldovich, Mit Csail. Proceedings of the VLDB Endowment, 2013

394 citations recorded by the source corpus.

MONOMI is a system for securely executing analytical workloads over sensitive data on an untrusted database server. MONOMI works by encrypting the entire database and running queries over the encrypted data. MONOMI introduces split client/server query execution, which can execute arbitrarily complex queries over encrypted data, as well as several techniques that improve performance for such workloads, including per-row precomputation, space-efficient encryption, grouped homomorphic addition, and pre-filtering. Since these optimizations are good for some queries but not others, MONOMI introduces a designer for choosing an efficient physical design at the server for a given workload, and a planner to choose an efficient execution plan for a given query at runtime. A prototype of MONOMI running on top of Postgres can execute most of the queries from the TPC-H benchmark with a median overhead of only 1.24× (ranging from 1.03×to 2.33×) compared to an un-encrypted Postgres database where a compromised server would reveal all data.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/03907b50cb57a058180e416529d521af8fdc3587

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

EnclaveDB: A Secure Database Using SGX (2018, methodology): “Systems such as CryptDB [1], Monomi [2], and Seabed [3] use these schemes to provide secure query processing while protecting”
zkLedger: Privacy-Preserving Auditing for Distributed Ledgers (2018, background): “There are many systems which compute on encrypted data to protect user confidentiality in the event of a server compromise [25, 31, 32, 40, 43, 44, 50].”
Observing and Preventing Leakage in MapReduce (2015, background): “TrustedDB [3], Cipherbase [2], and Monomi [29] use trusted hardware to process database queries over encrypted data, but do not protect the confidentiality and integrity of all code and data.”
M2R: Enabling Stronger Privacy in MapReduce Computation (2015, methodology): “Monomi [59] supports more complex queries, by adopting the download-and-compute approach for complex queries.”
Oblivious Multi-Party Machine Learning on Trusted Processors (2016, background): “Secure hardware TrustedDB [5], Cipherbase [3], and Monomi [64] use different forms of trusted hardware to process database queries with privacy.”
Opaque: An Oblivious and Encrypted Distributed Analytics Platform (2017, methodology): “The first type are encrypted databases, such as CryptDB [33], BlindSeer [31], Monomi [39], AlwaysEncrypted [26], and Seabed [30], that rely on cryptographic techniques for computation.”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, background): “Partially homomorphic encryption schemes [21, 41] are more practical, but limited in the range of supported operations [43, 46].”
VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, background): “TrustedDB [7], Cipherbase [6], and Monomi [64] use different forms of trusted hardware to”