TrustedDB: A Trusted Hardware-Based Database with Privacy and Data Confidentiality

By Sumeet Bajaj, Radu Sion. IEEE Transactions on Knowledge and Data Engineering, 2011

345 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/db5ecbd8c73ea5c7bd5697bf5aad08d40913c324

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 Information Systems

Citations in the corpus

Generic Attacks on Secure Outsourced Databases (2016, background): “In fact, our main reconstruction attacks only use adversarial knowledge of the distribution of queries and the data can be arbitrary.”
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.”
M2R: Enabling Stronger Privacy in MapReduce Computation (2015, background): “TrustedDB [7] demonstrated that a secure outsourced database solution can be built and run at a fraction of the monetary cost of any cryptography-enabled private data processing.”
EnclaveDB: A Secure Database Using SGX (2018, background): “Previous work that places small pieces of the query engine in trusted hardware, such as Cipherbase [12] and TrustedDB [13], does not provide confidentiality for queries or integrity and freshness for data.”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, methodology): “In addition, there is a tight constraint on the performance overhead since most computations on the data, especially data analytics tasks, consume vast numbers of CPU cycles which are directly billable [13, 5].”
This paper is included in the Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16). (neutral): “While in practice used to protect high-value secrets such as cryptographic keys [51], Bajaj and Sion [4, 5] demonstrate that secure co-processors can be used to split a database engine into trusted and untrusted parts.”
Opaque: An Oblivious and Encrypted Distributed Analytics Platform (2017, background): “The second type are databases, such as Haven [6], VC3 [34], TrustedDB [5], TDB [23] and GnatDb [40], that require trusted hardware to execute computation.”
Processing Analytical Queries over Encrypted Data (2013, methodology): “Collectively, this slows down queries 6, 14, and 18 by a much smaller amount.”
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”