Universally composable two-party and multi-party secure computation

By Ran Canetti, Yehuda Lindell, Rafail M. Ostrovsky, Amit Sahai. 2002

791 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (2018, methodology): “We refer the reader to Canetti et al. (2002) for formal deﬁnitions for such commit-and-prove schemes, and to Tramèr et al. (2017) who show how to trivially instantiate them using a TEE.”
Geppetto: Versatile Verifiable Computation (2015, methodology): “Canetti et al. [18] deﬁne CP schemes in the UC model and realize such schemes in the F ZK -hybrid model.”
Multiparty Computation from Somewhat Homomorphic Encryption (2012, background): “Under computational assumptions, it was shown in [6] how to construct UC-secure MPC protocols that handle the case where all but one of the parties are actively corrupted.”
Distributed Measurement with Private Set-Union Cardinality (2017, background): “General secure multiparty computation (MPC) protocols can realize any functionality including set-union cardinality, even in the UC model [1, 7], and recent advances in efficient MPC have been made in the multi-party, dishonest-majority setting that we require [10, 33, 34].”
Billion-Gate Secure Computation with Malicious Adversaries (2012, result): “Our results are in the standard model (i.e., no common reference strings or PKIs) and, in contrast to prior work, we do not use the random oracle model which has well-established theoretical shortcomings.”
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge (2017, background): “To see this, consider the ideal ZK functionality FZK [35], shown in Figure 9.”
Universally Composable Security with Global Setup (2007, methodology): “From this vantage point, we observe that the resource required to conduct the protocol simulations in [19] is a “trapdoor” for the CRS.”
Secure Multiparty Computation from SGX (2017, background): “For this setting, Katz shows that one can implement universally composable commitments [13], and hence arbitrary multiparty computation [14].”
An information-theoretic approach to privacy (2010, background): “In the secure multi-party computation model [21], each player wishes to keep hisentire input secret from the other players while jointly computing a function on all the inputs.”
Secure Multiparty Sorting Protocols with Covert Privacy (2016, background): “While starting out as a mere theoretical curiosity [17, 31, 10], both the protocols and the computing infrastructure have improved over the last two decades, such that significant real-world problems may be solved with them [8, 2, 3].”
Public Key Encryption That Allows PIR Queries (2007, methodology): “As we mentioned before, we can easily handle adaptive adversaries, by implementing hi using PRF’s, where the seeds are kept by the service provider, and users executing secure two-party computation protocols to get hi(w) for any w using [20] or, in the case of concurrent users, using [11] and having”
Semi-Homomorphic Encryption and Multiparty Computation (2011, result): “Related Work: It was shown by Canetti, Lindell, Ostrovsky and Sahai [ CLOS02 ] that secure computation is possible under general assumptions even when considering any corrupted number of parties in a concurrent setting (the UC framework).”