Practical Covertly Secure MPC for Dishonest Majority - Or: Breaking the SPDZ Limits

By Ivan Bjerre Damgård, Marcel Keller, Enrique Larraia, Valerio Pastro, Peter Scholl, Nigel P. Smart. 2013

552 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Secure Evaluation of Quantized Neural Networks (2019, methodology): “For the more general case, the bit-generation procedures by Damg ˚ ard [15] (for F p ) and Damg ˚ ard [16] (for Z 2 k ) are more suitable.”
A generic framework for privacy preserving deep learning (2018, methodology): “The MPC toolbox proposed in our framework implements the SPDZ protocol from [3, 2].”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “That said, there have been great advances in practical general-purpose MPC protocols of late [12,13,23,45,46,73,89,91,93,98].”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “This method of deferring comparisons to the end is similar to the deferred MAC veri cation of the TinyOT and SPDZ protocols [16, 18, 32]; we use this method throughout.”
Smart meter aggregation via secret-sharing (2013, background): “The multiplication of two secrets requires some interaction between the parties that hold shares, as well as pre-shared state [5].”
From Keys to Databases - Real-World Applications of Secure Multi-Party Computation (2018, methodology): “In addition, Jana supports encrypted secret shares suitable for linear secret sharing based processing of most query operators using the SPDZ protocol developed by Damgård et al. [11].”
Fairness in an Unfair World: Fair Multiparty Computation from Public Bulletin Boards (2017, methodology): “Our implementation consists of three major pieces: the bulletin board instantiated using Bitcoin, the MPC protocol instantiated using the SPDZ framework [3, 29], and a “witness decryptor” instantiated using an Intel SGX secure enclave.”
Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data (2015, methodology): “One possibility for obtaining security against malicious adversaries is to use the compute with MACs approach [6, 11, 10] to protect the shared values from being manipulated by a malicious adversary.”
Efficient Constant-Round Multi-party Computation Combining BMR and SPDZ (2019, methodology): “The numbers that are listed in Table 1 refer (in milli-seconds) to the SPDZ offline phase, as described in [11], with covert security and a 20% probability of cheating, using finite fields of size 128-bits.”
SPDℤ2k: Efficient MPC mod 2k for Dishonest Majority (2018, unknown): “Using these MACs, we can create an information-theoretically secure MPC protocol over Z2k in the preprocessing model, similar to the online phase of SPDZ from [7].”
MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer (2016, background): “However, this does not give the beneﬁts of general arithmetic circuits that allow integer operations.”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “Generating random bits modulo 2 k is not as simple as applying standard techniques from the ﬁeld setting [18] since this relies on taking square roots modulo p , but square roots modulo a power of 2 have a more complex structure, so this cannot be directly applied.”
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning (2019, unknown): “tocol [12, 13].”
Secure and Efficient Federated Transfer Learning (2019, methodology): “For the actively secure case, in contrast to our work over fields i.e. modulo prime, another direction can be an implementation over rings of the form 2k using SPDZ-2k protocol [23].”