SecureNN: Efficient and Private Neural Network Training

125 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

SplitNN-driven Vertical Partitioning (2020, background): “SecureNN (Sameer et al., 2018) was proposed in 2018 achieved great success in reducing the communication by over 8 times and in eliminating the requirement to use conventional cost intensive oblivious transfer protocols.”
Secure Evaluation of Quantized Neural Networks (2019, methodology): “SecureNN [63] can be seen as an extension of SecureML where both three-and four-party protocols (both with one corrupted party) are used.”
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019, background): “While several more recent work design MPC-based private learning solutions with information-theoretic security (Wagh et al., 2018; Mohassel & Rindal, 2018), their constructions are limited to three or four parties.”
CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “The prospect of such applications of machine learning with rigorous privacy and security guarantees has spurred a number of studies on machine learning via secure MPC [38, 41, 48, 58, 63, 66, 67].”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, methodology): “The state-of-the-art three-party (3PC) PPML frameworks in the honest majority setting such as ABY3 [5], SecureNN [6], and ASTRA [25] (prediction only) have fast and efficient protocols for the semi-honest case but are significantly slower when it comes to the malicious setting.”