Improved Primitives for Secure Multiparty Integer Computation

By Octavian Catrina, Sebastiaan de Hoogh. 2010

263 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): “We refer the reader to [9] and [17] for details on these.”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, methodology): “Subprotocols for these tasks are well-studied when the computation is over F p [19], [20], [21], but it is not immediately clear whether these techniques apply directly to the ring setting over Z 2 k .”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “Subsequently, the parties compute 〈x〉 = ∑ p∈P〈[x]p〉 using a carry-lookahead adder in log2(|P|) log2(L) communication rounds [14, 21].”
Smart meter aggregation via secret-sharing (2013, methodology): “We are aware of more efficient comparison protocols [3].”
Secure Computation on Floating Point Numbers (2013, methodology): “The most efficient implementation of this operation in our framework that we are aware of is due to Catrina and de Hoogh [15] that uses 2 rounds and 3n − 1 interactive operations, but works only on non-zero elements of a field.”
Data-oblivious graph algorithms for secure computation and outsourcing (2013, methodology): “Comparisons such as less-than and equality tests can be performed efficiently using, for instance, techniques of [13].”
SecureNN: Efficient and Private Neural Network Training (2018, methodology): “Since we are in the 3-server setting, another tempting option might be to use techniques from Sharemind [9] and its successors [10, 14] that use techniques of bit extraction to perform the”
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation (2015, methodology): “Arithmetic circuits for computing various primitives have been proposed in [17], [18].”
Efficient, Oblivious Data Structures for MPC (2014, background): “Catrina and de Hoogh [6] give an implementation with constant complexity that requires O ( (cid:96) ) secret random bits for statistical security. computes a bit indicating whether a is less than b .”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Some provide secure integer arithmetic [20], [21], [22], [23], [24], [25], [26], floating point arithmetic [27], [28], shuffling and sorting [29] and linking [4].”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, methodology): “Catrina and de Hoogh [19] have shown how to optimize this simple approach with computation modulo a prime, later adapted by Dalskov et al. [26] for computation modulo a power of two and by Escudero et al. [34] for switching to binary computation for parts of it.”
SecureNN: 3-Party Secure Computation for Neural Network Training (2019, background): “Secure multi-party computation (MPC) [8, 17, 25, 40] and specifically 3-party computation [6, 9, 10, 14, 18, 23, 28, 32] provide a generic solution for the above problem.”
Combining Differential Privacy and Secure Multiparty Computation (2015, background): “Equality tests are not considered in [24] but the best equality-checking protocol in [7] makes 8 openings of secret values for 64-bit integers in the online phase (and much more in the precomputing phase), i.”