Obliv-C: A Language for Extensible Data-Oblivious Computation

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How researchers see this paper

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Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “This arrangement incurs some overhead relative to using Obliv-C’s native types.”
Blind Justice: Fairness with Encrypted Sensitive Attributes (2018, methodology): “Several generic protocols for MPC exists, such as the ones based on arithmetic sharing (Damg˚ard et al., 2012), garbled circuits (Yao, 1986), or GMW (Goldreich et al., 1987), with several available implementations (Demmler et al., 2015b; Zahur & Evans, 2015b).”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, unknown): “Last major update EMP-toolkit [122] GC 2 09/2018 Obliv-C [129] GC 2 06/2018 ObliVM [97] GC 2 02/2016 TinyGarble [118] GC 2 10/2018 Wysteria [111] MC 2+ 10/2014 ABY [48] GC,MC 2 10/2018 SCALE-MAMBA - Hy 2+ 10/2018 Sharemind [24] Hy 3 09/2018 PICCO [134] Hy 3+ 10/2017 Frigate [101] 2+ - 05/2016 CBMC-G”
Secure Computation for Machine Learning With SPDZ (2019, methodology): “The work of Gascon et al. [2] uses the framework for semi-honest computation Obliv-C [9] and proposes several different methods for solving systems of linear equations.”
Secure Linear Regression on Vertically Partitioned Datasets (2016, methodology): “In particular, we used the framework Obliv-C extended with a bigint library to handle fixed point arithmetic computations with arbitrary precision.”