SecTL: Secure and Verifiable Transfer Learning-based inference - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

SecTL: Secure and Verifiable Transfer Learning-based inference

Résumé

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.
Fichier principal
Vignette du fichier
ICISSP_2022_93_CR.pdf (415.06 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

cea-04470272 , version 1 (21-02-2024)

Licence

Identifiants

Citer

Abbass Madi, Oana Stan, Renaud Sirdey, Cedric Gouy-Pailler. SecTL: Secure and Verifiable Transfer Learning-based inference. ICISSP 2022 - the 8th International Conference on Information Systems Security and Privacy, Feb 2022, Virtual, France. pp.220-229, ⟨10.5220/0010987700003120⟩. ⟨cea-04470272⟩
20 Consultations
9 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More