General Secure Multi-party Computation from any Linear Secret-Sharing Scheme

By Ronald Cramer, Ivan Bjerre Damgård, Ueli M. Maurer. International Conference on the Theory and Application of Cryptographic Techniques, 2000

599 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/64856147da1c26464f183f13be9137d60e23a729

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Citations in the corpus

Privacy-Preserving Set Operations (2005, unknown): “Recent results that allow noninteractive private multiplication of shares [7] do not extend to our adversary model, in which any c < n players may collude.”
Improved Primitives for Secure Multiparty Integer Computation (2010, methodology): “The protocols can be instantiated using secret sharing [8] or homomorphic encryption [9].”
Secure Computation on Floating Point Numbers (2013, methodology): “Thus, security of our protocols in the malicious model can be achieved by using standard VSS techniques, e.g., [27], [18], where a range proof, e.g., [37] will be additionally needed for the building blocks.”
Unconditionally Secure Constant Round Multi-Party Computation for Equality, Comparison, Bits and Exponentiation (2006, background): “It can then be shown that an information theoretic VSS with a multiplication protocol implements this ideal functionality (as an example the VSS schemes from [ CDM00 ] will do).”
Secret-Sharing Schemes: A Survey (2011, background): “, Byzantine agreement [54], secure multiparty computations [13, 24, 28], threshold cryptography [31], access control [52], attribute-based encryption [40, 68], and generalized oblivious transfer [59, 65].”
Secure Computation with Fixed-Point Numbers (2010, methodology): “We use standard techniques for constructing multiparty computation protocols based on secret sharing, similar to [6,7,17,5].”
L1 - An Intermediate Language for Mixed-Protocol Secure Computation (2011, methodology): “It implements the basic protocols for [10] as objects.”
Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, background): “n be collectively computed over a dataset owned by multiple parties using their own inputs (i.e., a subset of the dataset) so that any party learns nothing about others’ data except the outputs [53]– [55]. Speciﬁcally, nparties P 1;P 2;::;P n own npieces of private data X 1;X 2;:::;X n, respectively”
Distributed Pseudo-random Functions and KDCs (1999, methodology): “Recently MSPs were used by Cramer, Damgard, and Maurer [16] to construct multi-party computation protocols for general monotone sets of subsets of players, any one of which may consist of cheaters.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, background): “CCD [31] rely on secret-sharing schemes supporting strong multiplication [40], [33], [29], [41] rather than on public-key primitives.”