SecTL: Secure and Verifiable Transfer Learning-based inference

Abbass Madi; Oana Stan; Renaud Sirdey; Cédric Gouy-Pailler

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

SecTL: Secure and Verifiable Transfer Learning-based inference

Topics: Authentication, Privacy and Security Models; Security and Privacy in Cloud and Pervasive Computing; Trusted Computing

In Proceedings of the 8th International Conference on Information Systems Security and Privacy ICISSP - Volume 1, 220-229, 2022

Authors: Abbass Madi ; Oana Stan ; Renaud Sirdey and Cédric Gouy-Pailler

Affiliation: Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France

Keyword(s): Homomorphic Encryption, Verifiable Computing,Transfer Learning.

Abstract: This paper investigates the possibility of realizing complex machine learning tasks over encrypted inputs with guaranteed integrity. Our approach combines Fully Homomorphic Encryption (FHE) and Verifiable Computing (VC) to achieve these properties. To workaround the practical difficulties when using these techniques - high computational cost for FHE and limited expressivity for VC, we leverage on transfer learning as a mean to (legitimately) decrease the footprint of encrypted domain calculations without jeopardizing the target security properties. In that sense, our approach demonstrates that scaling confidential and verifiable encrypted domain calculations to complex machine learning functions does not necessarily require scaling these techniques to the evaluation of large models. We furthermore demonstrate the practicality of our approach on an image classification task.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.145.175.243

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:

Madi, A.; Stan, O.; Sirdey, R. and Gouy-Pailler, C. (2022). SecTL: Secure and Verifiable Transfer Learning-based inference. In Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP; ISBN 978-989-758-553-1; ISSN 2184-4356, SciTePress, pages 220-229. DOI: 10.5220/0010987700003120

@conference{icissp22,
author={Abbass Madi. and Oana Stan. and Renaud Sirdey. and Cédric Gouy{-}Pailler.},
title={SecTL: Secure and Verifiable Transfer Learning-based inference},
booktitle={Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP},
year={2022},
pages={220-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010987700003120},
isbn={978-989-758-553-1},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP
TI - SecTL: Secure and Verifiable Transfer Learning-based inference
SN - 978-989-758-553-1
IS - 2184-4356
AU - Madi, A.
AU - Stan, O.
AU - Sirdey, R.
AU - Gouy-Pailler, C.
PY - 2022
SP - 220
EP - 229
DO - 10.5220/0010987700003120
PB - SciTePress