Abstract
In this paper, we provide experimental evidence for the benefits of multi-task learning in the context of masked AES implementations (via the ASCADv1-r and ASCADv2 databases). We develop an approach for comparing single-task and multi-task approaches rather than comparing specific resulting models: we do this by training many models with random hyperparameters (instead of comparing a few highly tuned models). We find that multi-task learning has significant practical advantages that make it an attractive option in the context of device evaluations: the multi-task approach leads to performant networks quickly in particular in situations where knowledge of internal randomness is not available during training.
Original language | English |
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Title of host publication | Applied Cryptography and Network Security Workshops - ACNS 2023 Satellite Workshops, ADSC, AIBlock, AIHWS, AIoTS, CIMSS, Cloud S and P, SCI, SecMT, SiMLA, Proceedings |
Publisher | Springer |
Pages | 121-138 |
Number of pages | 18 |
ISBN (Electronic) | 9783031411816 |
ISBN (Print) | 9783031411809 |
DOIs | |
Publication status | Published - 4 Oct 2023 |
Event | 21st International Conference on Applied Cryptography and Network Security, ACNS 2023 - Kyoto, Japan Duration: 19 Jun 2023 → 22 Jun 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13907 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 21st International Conference on Applied Cryptography and Network Security, ACNS 2023 |
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Country/Territory | Japan |
City | Kyoto |
Period | 19/06/23 → 22/06/23 |
Bibliographical note
Funding Information:Thomas Marquet has been supported by the KWF under grant number KWF-3520-31870-45842. Elisabeth Oswald has been supported in part by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 725042).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Deep Learning
- Masking
- Multi-Task Learning
- Side Channel Attacks
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science