Towards practical secure neural network inference: the journey so far and the road ahead
Neural networks (NNs) have become one of the most important tools for artificial
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
intelligence. Well-designed and trained NNs can perform inference (eg, make decisions or …
{ABY2. 0}: Improved {Mixed-Protocol} secure {Two-Party} computation
Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly
evaluate a function on their private inputs while maintaining input privacy. In this work, we …
evaluate a function on their private inputs while maintaining input privacy. In this work, we …
BLAZE: blazing fast privacy-preserving machine learning
Machine learning tools have illustrated their potential in many significant sectors such as
healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential …
healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential …
Fantastic four:{Honest-Majority}{Four-Party} secure computation with malicious security
This work introduces a novel four-party honest-majority MPC protocol with active security
that achieves comparable efficiency to equivalent protocols in the same setting, while having …
that achieves comparable efficiency to equivalent protocols in the same setting, while having …
Piranha: A {GPU} platform for secure computation
Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine
learning (ML). However, secure training of large-scale ML models currently requires a …
learning (ML). However, secure training of large-scale ML models currently requires a …
SoK: cryptographic neural-network computation
We studied 53 privacy-preserving neural-network papers in 2016-2022 based on
cryptography (without trusted processors or differential privacy), 16 of which only use …
cryptography (without trusted processors or differential privacy), 16 of which only use …
Orca: Fss-based secure training and inference with gpus
Secure Two-party Computation (2PC) allows two parties to compute any function on their
private inputs without revealing their inputs to each other. In the offline/on-line model for …
private inputs without revealing their inputs to each other. In the offline/on-line model for …
Concretely efficient secure multi-party computation protocols: survey and more
D Feng, K Yang - Security and Safety, 2022 - sands.edpsciences.org
Secure multi-party computation (MPC) allows a set of parties to jointly compute a function on
their private inputs, and reveals nothing but the output of the function. In the last decade …
their private inputs, and reveals nothing but the output of the function. In the last decade …
A survey of trustworthy federated learning with perspectives on security, robustness and privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
benefited human society. Among various AI technologies, Federated Learning (FL) stands …
" Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security Conferences
D Olszewski, A Lu, C Stillman, K Warren… - Proceedings of the …, 2023 - dl.acm.org
Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …
seemingly contradictory results and expands the boundaries of known discoveries …