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secureTF: A Secure TensorFlow Framework
[article]
2021
arXiv
pre-print
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2020
Proceedings of the 21st International Middleware Conference
unpublished
doi:10.1145/3423211.3425687 fatcat:bez6qbu6xjgavjijponlsznasa
doi:10.1145/3423211.3425687 fatcat:bez6qbu6xjgavjijponlsznasa
To tackle this challenge, we designed secureTF, a distributed secure machine learning framework based on Tensorflow for the untrusted cloud infrastructure. secureTF is a generic platform to support unmodified ...
TensorFlow applications, while providing end-to-end security for the input data, ML model, and application code. secureTF is built from ground-up based on the security properties provided by Trusted Execution ...
To overcome these design challenges, we present secureTF, a secure machine learning framework for the untrusted infrastructure. ...
arXiv:2101.08204v1
fatcat:w5zjlifjrfae5az6yia2owbvre
Plinius: Secure and Persistent Machine Learning Model Training
[article]
2021
arXiv
pre-print
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We present PLINIUS, a ML framework using Intel SGX enclaves for secure training of ML models and PM for fault tolerance guarantees. ...
after a system failure. ...
SecureTF [25] integrates TensorFlow ML library for model training and inference in secure SCONE containers. ...
arXiv:2104.02987v2
fatcat:5btty7krfncmdj6adrbl4xqmje
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX
[article]
2021
arXiv
pre-print
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Cloud deployment with various ML models shows that Citadel scales to a large number of enclaves with less than 1.73X slowdown caused by SGX. ...
This paper presents Citadel, a scalable collaborative ML system that protects the privacy of both data owner and model owner in untrusted infrastructures with the help of Intel SGX. ...
We also assume that the participants trust standard ML frameworks like TensorFlow [2] and PyTorch [68] . ...
arXiv:2105.01281v2
fatcat:iuc2gbqh4rbpfou2bf7hf7j4em
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
2021
IEEE Access
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This paper conducts a systematic and comprehensive survey by classifying attack vectors and mitigation in TEE-protected confidential ML computation in the untrusted environment, analyzes the multi-party ...
ML security requirements, and discusses related engineering challenges. ...
TensorSCONE [26] introduces TensorFlow to SCONE containers [51] , while secureTF [15] introduces a secure distributed machine learning framework built upon TensorFlow and SGX. ...
doi:10.1109/access.2021.3136889
fatcat:scrytvepkjafxblcqg3gjk5vqu
Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2111.03308v1 fatcat:3eh6kiaxm5ad7guyntxk6pg6miOther Versions
This paper conducts a systematic and comprehensive survey by classifying attack vectors and mitigation in TEE-protected confidential ML computation in the untrusted environment, analyzes the multi-party ...
ML security requirements, and discusses related engineering challenges. ...
TensorSCONE [26] introduces TensorFlow to SCONE containers [51] , while secureTF [15] introduces a secure distributed machine learning framework built upon TensorFlow and SGX. ...
arXiv:2111.03308v2
fatcat:kmklsqvzureilldvr4ui4azrwi