On the challenge of hardware errors, adversarial attacks and privacy leakage for embedded machine learning

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Machine Learning deployment in Embedded Systems and Edge devices offer interesting advantages compared with the Cloud-based approaches, especially from a power consumption and environmental impact perspective. However, two principal problems need to be addressed towards trustworthy Embedded ML; first, Robustness to errors: several sources of faults can jeopardize ML systems integrity; be it hardware failures, as well as malicious fault injection. Second, Security and Privacy: this includes adversarial attacks and information leakage.
Original languageEnglish
Title of host publicationEmbedded machine learning for cyber-physical, IoT, and edge computing: use cases and emerging challenges
EditorsSudeep Pasricha, Muhammad Shafique
Place of PublicationCham
PublisherSpringer Nature Switzerland
Pages497-517
Number of pages21
ISBN (Electronic)9783031406775
ISBN (Print)9783031406768
DOIs
Publication statusPublished - 07 Oct 2023

Fingerprint

Dive into the research topics of 'On the challenge of hardware errors, adversarial attacks and privacy leakage for embedded machine learning'. Together they form a unique fingerprint.

Cite this