CryptDB: protecting confidentiality with encrypted query processing

By Raluca Ada Popa, Catherine M. S. Redfield, Nickolai Zeldovich, Hari Balakrishnan. Symposium on Operating Systems Principles, 2011

1,446 citations recorded by the source corpus.

Online applications are vulnerable to theft of sensitive information because adversaries can exploit software bugs to gain access to private data, and because curious or malicious administrators may capture and leak data. CryptDB is a system that provides practical and provable confidentiality in the face of these attacks for applications backed by SQL databases. It works by executing SQL queries over encrypted data using a collection of efficient SQL-aware encryption schemes. CryptDB can also chain encryption keys to user passwords, so that a data item can be decrypted only by using the password of one of the users with access to that data. As a result, a database administrator never gets access to decrypted data, and even if all servers are compromised, an adversary cannot decrypt the data of any user who is not logged in. An analysis of a trace of 126 million SQL queries from a production MySQL server shows that CryptDB can support operations over encrypted data for 99.5% of the 128,840 columns seen in the trace. Our evaluation shows that CryptDB has low overhead, reducing throughput by 14.5% for phpBB, a web forum application, and by 26% for queries from TPC-C, compared to unmodified MySQL. Chaining encryption keys to user passwords requires 11--13 unique schema annotations to secure more than 20 sensitive fields and 2--7 lines of source code changes for three multi-user web applications.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/acc104820d211a25b568f8677b4ad29a8667c7cd

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Citations in the corpus

An Ideal-Security Protocol for Order-Preserving Encoding (2013, background): “Keywords-order-preserving encryption, encoding I. INTRODUCTION Encryption is a powerful technique for protecting confidential data stored on an untrusted server, such as in cloud computing [10, 12, 37].”
The Efficient Server Audit Problem, Deduplicated Re-execution, and the Web (2017, background): “Also, MediaWiki and phpBB are in common use, and HotCRP has become a reference point for systems security publications that deal with PHP-based web applications [26, 52, 67, 68, 72, 93].”
Observing and Preventing Leakage in MapReduce (2015, background): “CryptDB [22] and MrCrypt [27] use partial homomorphic encryption to run some computations on encrypted data; they neither protect confidentiality of code, nor guarantee the integrity of results.”
Order-Preserving Encryption Revisited: Improved Security Analysis and Alternative Solutions (2011, background): “[19] R.”
Verifying computations without reexecuting them (2015, background): “Across computer systems, we are starting to see a new style of work: reducing sophisticated cryptography and other achievements of theoretical computer science to practice [28, 35, 38, 49].”
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.”
M2R: Enabling Stronger Privacy in MapReduce Computation (2015, methodology): “CryptDB [47] takes a purely cryptographic approach, showing the practicality of using partially homomorphic encryption schemes [3, 15, 45, 46, 54].”
Generic Attacks on Secure Outsourced Databases (2016, background): “…be applied to this problem, ranging from solutions based on tools such as fully homomorphic encryption (FHE) [20, 46] or oblivious RAM (ORAM) [23, 25] to more practical solutions based on weaker primitives such as structural encryption [33] or deterministic and order-preserving encryption [42, 2].”
Inference Attacks on Property-Preserving Encrypted Databases (2015, neutral): “This process is referred to as peeling in [35] and every cell in a given column is peeled to the same level.”
PrivateClean: Data Cleaning and Differential Privacy (2016, background): “Another approach is homomorphic encryption that allows for data processing directly on the encrypted data [38].”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, background): “Partially homomorphic encryption schemes [31, 15] are more practical, but limited in the range of supported operations [33, 36].”
The real cost of sequencing: scaling computation to keep pace with data generation (2016, background): “Research in this field can broadly be split into two layers: first, sensitive data must be protected from leaking to a third party [49] and second, the cloud service provider should be made as oblivious as possible to the computation [50].”
EnclaveDB: A Secure Database Using SGX (2018, methodology): “EnclaveDB supports a much simpler model for key management compared to existing systems [1], [12] which requires users to associate and manage encryption keys for each column containing sensitive data.”
SplitX: high-performance private analytics (2013, background): “Some systems store user data in an encrypted form and perform queries over that data [24, 27, 30].”