ARTMAN'25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The ARTMAN workshop aims to bring together academic researchers and industry practitioners from diverse domains, primarily security & privacy and machine learning, but also various application fields, to collaboratively explore and discuss resilient and trustworthy machine learning-powered applications and systems. This workshop focuses on AI/ML application domains and welcomes contributions on both foundational and applied aspects of ML across various industries, including transportation, aerospace, healthcare, energy, and finance, among others, showcasing AI-driven advances in performance and efficiency. This workshop also seeks contributions on the application of reliable and secure AI/ML algorithms, especially knowledge-informed approaches, to improve resilience and trust, particularly in human-machine partnerships and interactions within such scenarios.

Original languageEnglish
Title of host publicationCCS 2025 - Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery, Inc
Pages4894-4895
Number of pages2
ISBN (Electronic)9798400715259
DOIs
Publication statusPublished - 22 Nov 2025
Event32nd ACM SIGSAC Conference on Computer and Communications Security, CCS 2025 - Taipei, Taiwan, Province of China
Duration: 13 Oct 202517 Oct 2025

Publication series

NameCCS 2025 - Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security

Conference

Conference32nd ACM SIGSAC Conference on Computer and Communications Security, CCS 2025
Country/TerritoryTaiwan, Province of China
CityTaipei
Period13/10/2517/10/25

Keywords

  • Machine Learning Systems
  • Resilience
  • Trust

Fingerprint

Dive into the research topics of 'ARTMAN'25: Third Workshop on Recent Advances in Resilient and Trustworthy MAchine learning-driveN systems'. Together they form a unique fingerprint.

Cite this