Practical secure computation outsourcing: A survey
The rapid development of cloud computing promotes a wide deployment of data and
computation outsourcing to cloud service providers by resource-limited entities. Based on a …
computation outsourcing to cloud service providers by resource-limited entities. Based on a …
MP-SPDZ: A versatile framework for multi-party computation
M Keller - Proceedings of the 2020 ACM SIGSAC conference on …, 2020 - dl.acm.org
Multi-Protocol SPDZ (MP-SPDZ) is a fork of SPDZ-2 (Keller et al., CCS'13), an
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …
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 …
ABY3 A Mixed Protocol Framework for Machine Learning
P Mohassel, P Rindal - Proceedings of the 2018 ACM SIGSAC …, 2018 - dl.acm.org
Machine learning is widely used to produce models for a range of applications and is
increasingly offered as a service by major technology companies. However, the required …
increasingly offered as a service by major technology companies. However, the required …
A pragmatic introduction to secure multi-party computation
Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s
to a tool for building real systems today. Over the past decade, MPC has been one of the …
to a tool for building real systems today. Over the past decade, MPC has been one of the …
Chameleon: A hybrid secure computation framework for machine learning applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …
evaluation (SFE) which enables two parties to jointly compute a function without disclosing …
{XONN}:{XNOR-based} oblivious deep neural network inference
Advancements in deep learning enable cloud servers to provide inference-as-a-service for
clients. In this scenario, clients send their raw data to the server to run the deep learning …
clients. In this scenario, clients send their raw data to the server to run the deep learning …
Deepsecure: Scalable provably-secure deep learning
This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL)
framework that is built upon automated design, efficient logic synthesis, and optimization …
framework that is built upon automated design, efficient logic synthesis, and optimization …
Privacy-preserving federated deep learning with irregular users
Federated deep learning has been widely used in various fields. To protect data privacy,
many privacy-preservingapproaches have been designed and implemented in various …
many privacy-preservingapproaches have been designed and implemented in various …
Oblivm: A programming framework for secure computation
We design and develop ObliVM, a programming framework for secure computation. ObliVM
offers a domain specific language designed for compilation of programs into efficient …
offers a domain specific language designed for compilation of programs into efficient …