loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hsiao-Ying Lin ; Chengfang Fang and Jie Shi

Affiliation: Huawei International, Singapore

Keyword(s): Machine Learning Security, Deep Neural Networks, Learning Model Protection, Model Confidentiality.

Abstract: Deep neural networks are widely deployed in a variety of application areas to provide real-time inference services, such as mobile phones, autonomous vehicles and industrial automation. Deploying trained models in end-user devices rises high demands on protecting models against model stealing attacks. To tackle this concern, applying cryptography algorithms and using trusted execution environments have been proposed. However, both approaches cause significant overhead on inference time. With the support of trusted execution environment, we propose bident-structure networks to protect the neural networks while maintaining inference efficiency. Our main idea is inspired by the secret-sharing concept from cryptography community, where we treat the neural network as the secret to be protected. We prove the feasibility of bident-structure methods by empirical experiments on MNIST. Experimental results also demonstrate that efficiency overhead can be reduced by compressing sub-networks run ning in trusted execution environments. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.116.8.110

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Lin, H.; Fang, C. and Shi, J. (2020). Bident Structure for Neural Network Model Protection. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-399-5; ISSN 2184-4356, SciTePress, pages 377-384. DOI: 10.5220/0008923403770384

@conference{icissp20,
author={Hsiao{-}Ying Lin. and Chengfang Fang. and Jie Shi.},
title={Bident Structure for Neural Network Model Protection},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - ICISSP},
year={2020},
pages={377-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008923403770384},
isbn={978-989-758-399-5},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - ICISSP
TI - Bident Structure for Neural Network Model Protection
SN - 978-989-758-399-5
IS - 2184-4356
AU - Lin, H.
AU - Fang, C.
AU - Shi, J.
PY - 2020
SP - 377
EP - 384
DO - 10.5220/0008923403770384
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic