Secure Computation with Fixed-Point Numbers

By Octavian Catrina, Amitabh Saxena. 2010

268 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, background): “For its usage in the context of secure multiparty computation, see for example Catrina and Saxena [9].”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, background): “Adapting the scheme to floating point numbers requires implementing different arithmetic operation protocols [13] or encoding floating point numbers as integers, e.”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, background): “aster execution for floating-point operations. Most methods for secure computation on floating and fixed point numbers proposed in the literature were realized in Shamir’s secret sharing scheme, e.g. [2, 29, 58, 79, 91], but some of them also in GC [79], GMW [35], and HE [67] based schemes. The qual”
Fast, Privacy Preserving Linear Regression over Distributed Datasets based on Pre-Distributed Data (2015, methodology): “For that purpose we use a slightly modified version of the truncation protocol of Catrina and Saxena [9].”
Generalizing the SPDZ Compiler For Other Protocols (2018, methodology): “This library is based on a body of literature [2, 10, 11] that uses techniques such as statistical masking to implement such operations without having to rely solely on field-arithmetic circuits.”
Secure multiparty aggregation with differential privacy: a comparative study (2013, background): “Adapting the scheme to floating point numbers requires implementing different arithmetic operation protocols [10], or encoding floating point numbers as integers, e.”
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020, methodology): “Finally, the (statistical) privacy guarantees of the truncation protocol follows from [6].”
Secure Computation on Floating Point Numbers (2013, methodology): “While prior literature [23], [17], [24] offers a limited number of techniques for real number computation , we are not aware of any work that develops a suite of techniques for floating point computation or secure multi-party techniques for complex functions such as logarithm, square root, and…”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, methodology): “We use the specific choices of initializations given in [27, 28] to efficiently compute division over secret shares.”
Differentially private data aggregation with optimal utility (2014, methodology): “The following SMPC protocols [8, 13, 17, 25] are used for our DDP mechanisms.”
L1 - An Intermediate Language for Mixed-Protocol Secure Computation (2011, background): “Various cryptographic protocols for secure twoparty and multi-party division protocols have been proposed (e.g., [2], [23], [7], [ 9 ]).”
Improved Primitives for Secure Multiparty Integer Computation (2010, methodology): “We start by reviewing an efficient protocol introduced in [6].”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, background): “Catrina and Saxena first suggested this for computation modulo a prime [20], Dalskov et al.”
Practical Covertly Secure MPC for Dishonest Majority - Or: Breaking the SPDZ Limits (2013, background): “The reader should think p ≈ 2(32), 2(64), 2(128) and the type of operations envisaged in [8, 9] etc.”
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].”