1. vSQL: Verifying Arbitrary SQL Queries over Dynamic Outsourced Databases 2017 EncryptedDatabases Oakland VerifiableComputation
    Y. Zhang, D. Genkin, J. Katz, D. Papadopoulos, C. Papamanthou
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    author={Y. {Zhang} and D. {Genkin} and J. {Katz} and D. {Papadopoulos} and C. {Papamanthou}},
    booktitle={2017 IEEE Symposium on Security and Privacy (SP)},
    title={vSQL: Verifying Arbitrary SQL Queries over Dynamic Outsourced Databases},
    keywords={cloud computing;cryptographic protocols;database management systems;formal verification;outsourcing;polynomials;query processing;SQL;verifiable SQL queries;vSQL;dynamic outsourced databases;cloud database systems;cryptographic protocol;CMT interactive proof protocol;polynomial delegation protocol;cost polylogarithmic verification;succinct arguments of knowledge;SNARK;Servers;Databases;Protocols;Structured Query Language;Cryptography;Outsourcing},

Cloud database systems such as Amazon RDS or Google Cloud SQL enable the outsourcing of a large database to a server who then responds to SQL queries. A natural problem here is to efficiently verify the correctness of responses returned by the (untrusted) server. In this paper we present vSQL, a novel cryptographic protocol for publicly verifiable SQL queries on dynamic databases. At a high level, our construction relies on two extensions of the CMT interactive-proof protocol [Cormode et al., 2012]: (i) supporting outsourced input via the use of a polynomial-delegation protocol with succinct proofs, and (ii) supporting auxiliary input (i.e., non-deterministic computation) efficiently. Compared to previous verifiable-computation systems based on interactive proofs, our construction has verification cost polylogarithmic in the auxiliary input (which for SQL queries can be as large as the database) rather than linear. In order to evaluate the performance and expressiveness of our scheme, we tested it on SQL queries based on the 1PC-H benchmark on a database with 6 x 106 rows and 13 columns. The server overhead in our scheme (which is typically the main bottleneck) is up to 120 x lower than previous approaches based on succinct arguments of knowledge (SNARKs), and moreover we avoid the need for query-dependent pre-processing which is required by optimized SNARK-based schemes. In our construction, the server/client time and the communication cost are comparable to, and sometimes smaller than, those of existing customized solutions which only support specific queries.