Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption

Fabian Schmid; Shibam Mukherjee; Stjepan Picek; Marc Stöttinger; Fabrizio De Santis; Christian Rechberger

doi:10.1007/978-3-031-57543-3_8

Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption

Fabian Schmid^*, Shibam Mukherjee, Stjepan Picek, Marc Stöttinger, Fabrizio De Santis, Christian Rechberger

^*Korrespondierende/r Autor/-in für diese Arbeit

Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics (98770)
Institut für Angewandte Informationsverarbeitung und Kommunikationstechnologie (7050)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

This work investigates using Homomorphic Encryption (HE) to assist the security evaluation of cryptographic devices without revealing side-channel information. For the first time, we evaluate the feasibility of execution of deep learning-based side-channel analysis on standard server equipment using an adapted HE protocol. By examining accuracy and execution time, it demonstrates the successful application of private SCA on both unprotected and protected cryptographic implementations. This contribution is a first step towards confidential side-channel analysis. Our study is limited to the honest-but-curious trust model, where we could reconstruct the secret of an unprotected AES implementation in seconds and of a masked AES implementation in under 17 min.

Originalsprache	englisch
Titel	Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings
Redakteure/-innen	Romain Wacquez, Naofumi Homma
Herausgeber (Verlag)	Springer Science and Business Media Deutschland GmbH
Seiten	133-154
Seitenumfang	22
ISBN (Print)	9783031575426
DOIs	https://doi.org/10.1007/978-3-031-57543-3_8
Publikationsstatus	Veröffentlicht - 2024
Veranstaltung	15th International Workshop on Constructive Side-channel Analysis and Secure Design: COSADE 2024 - 880, route de Mimet, Gardanne, Frankreich Dauer: 8 Apr. 2024 → 10 Apr. 2024 https://www.cosade.org/cosade24/program.html

Publikationsreihe

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band	14595 LNCS
ISSN (Print)	0302-9743
ISSN (elektronisch)	1611-3349

Konferenz

Konferenz	15th International Workshop on Constructive Side-channel Analysis and Secure Design
Kurztitel	COSADE
Land/Gebiet	Frankreich
Ort	Gardanne
Zeitraum	8/04/24 → 10/04/24
Internetadresse	https://www.cosade.org/cosade24/program.html

ASJC Scopus subject areas

Theoretische Informatik
Allgemeine Computerwissenschaft

Zugriff auf Dokument

10.1007/978-3-031-57543-3_8

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Schmid, F., Mukherjee, S., Picek, S., Stöttinger, M., De Santis, F., & Rechberger, C. (2024). Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption. in R. Wacquez, & N. Homma (Hrsg.), Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings (S. 133-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14595 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-57543-3_8

Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption. / Schmid, Fabian; Mukherjee, Shibam; Picek, Stjepan et al.
Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings. Hrsg. / Romain Wacquez; Naofumi Homma. Springer Science and Business Media Deutschland GmbH, 2024. S. 133-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14595 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Schmid, F, Mukherjee, S, Picek, S, Stöttinger, M, De Santis, F & Rechberger, C 2024, Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption. in R Wacquez & N Homma (Hrsg.), Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 14595 LNCS, Springer Science and Business Media Deutschland GmbH, S. 133-154, 15th International Workshop on Constructive Side-channel Analysis and Secure Design, Gardanne, Frankreich, 8/04/24. https://doi.org/10.1007/978-3-031-57543-3_8

Schmid F, Mukherjee S, Picek S, Stöttinger M, De Santis F, Rechberger C. Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption. in Wacquez R, Homma N, Hrsg., Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. S. 133-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-57543-3_8

Schmid, Fabian ; Mukherjee, Shibam ; Picek, Stjepan et al. / Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption. Constructive Side-Channel Analysis and Secure Design - 15th International Workshop, COSADE 2024, Proceedings. Hrsg. / Romain Wacquez ; Naofumi Homma. Springer Science and Business Media Deutschland GmbH, 2024. S. 133-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This work investigates using Homomorphic Encryption (HE) to assist the security evaluation of cryptographic devices without revealing side-channel information. For the first time, we evaluate the feasibility of execution of deep learning-based side-channel analysis on standard server equipment using an adapted HE protocol. By examining accuracy and execution time, it demonstrates the successful application of private SCA on both unprotected and protected cryptographic implementations. This contribution is a first step towards confidential side-channel analysis. Our study is limited to the honest-but-curious trust model, where we could reconstruct the secret of an unprotected AES implementation in seconds and of a masked AES implementation in under 17 min.",

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AU - De Santis, Fabrizio

AU - Rechberger, Christian

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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AB - This work investigates using Homomorphic Encryption (HE) to assist the security evaluation of cryptographic devices without revealing side-channel information. For the first time, we evaluate the feasibility of execution of deep learning-based side-channel analysis on standard server equipment using an adapted HE protocol. By examining accuracy and execution time, it demonstrates the successful application of private SCA on both unprotected and protected cryptographic implementations. This contribution is a first step towards confidential side-channel analysis. Our study is limited to the honest-but-curious trust model, where we could reconstruct the secret of an unprotected AES implementation in seconds and of a masked AES implementation in under 17 min.

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Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption

Abstract

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