Attaque side channel contre la confidentialité des modèles de machine learning embarqués : attaques, protections, évaluation

Raphaël Joud

DOI: 10.70675/4cb81553z489az4058z8c0dz4b44c781021c
Published: 2024-01-18
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Open access: yes

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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

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Cite this work

BibTeX

@article{allodium:10.70675/4cb81553z489az4058z8c0dz4b44c781021c,
  title = {Attaque side channel contre la confidentialité des modèles de machine learning embarqués : attaques, protections, évaluation},
  author = {Raphaël Joud},
  year = {2024},
  doi = {10.70675/4cb81553z489az4058z8c0dz4b44c781021c},
  url = {https://doi.org/10.70675/4cb81553z489az4058z8c0dz4b44c781021c}
}

RIS

TY  - JOUR
TI  - Attaque side channel contre la confidentialité des modèles de machine learning embarqués : attaques, protections, évaluation
AU  - Raphaël Joud
PY  - 2024
DO  - 10.70675/4cb81553z489az4058z8c0dz4b44c781021c
UR  - https://doi.org/10.70675/4cb81553z489az4058z8c0dz4b44c781021c
ER  -

APA

Joud, R. (2024). Attaque side channel contre la confidentialité des modèles de machine learning embarqués : attaques, protections, évaluation. https://doi.org/10.70675/4cb81553z489az4058z8c0dz4b44c781021c

Source records

hal · retrieved 2026-09-27T06:29:26.978Z