Scalable and Unconditionally Secure Multiparty Computation

By Ivan Bjerre Damgård, Jesper Buus Nielsen. Annual International Cryptology Conference, 2007

330 citations recorded by the source corpus.

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A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020, methodology): “information-theoretic privacy of the corresponding computations against T colluding clients [4, 3, 12].”
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019, methodology): “Furthermore, we empirically demonstrate the impact of CodedPrivateML by comparing it with the cryptographic approach based on secure multi-party computing (MPC) [9]–[12], that can also be applied to enable privacy-preserving machine learning tasks (e.g., see [13]–[18]).”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “For the case of Shamir sharing, we have several di erent instantiations; for example, multiplication can be computed using [25] or [17], veri cation can be carried out in one of two methods, and so on.”
Near-Linear Unconditionally-Secure Multiparty Computation with a Dishonest Minority (2012, methodology): “active t < n/3 unconditional O(cMn (2)κ) + poly(nκ) [18] active t < n/3 unconditional O(cMn log n+ dMn 2 log n) + poly(nκ) [16] active t < n/3 perfect O(cMn log n+ dMn 2 log n+ n(3) log n) [4]”