IEEE TAI Special Issue on Security and Privacy in Machine Learning (IEEE TAI SI: S&P in Machine Learning 2021)
Local: A confirmar
Data do evento: 01/07/2021 a 15/02/2022
Prazo de submissão de trabalhos: 15/08/2021
The ThemeMachine learning plays an increasingly important role in the field of artificial intelligence, such as image classification, computer vision, natural language processing, recommendation systems, etc.. Meanwhile, in the era of big data, both system security and data privacy are particularly important. Machine learning vulnerabilities and privacy preservation learning have attracted growing interest in the fields of artificial intelligence, information security, and data privacy. The aims of this special issue are: (1) to present the cutting-edge research about security and privacy in machine learning; (2) to provide a platform for researchers and practitioners to present their views on future research trends in building secure and privacy-preserving learning systems. Topics of interest include but are not limited to:Poisoning attack and defenseEvasion attacks and defenseGeneration techniques of adversarial examplesDetection techniques of adversarial examplesMitigation and defense techniques of adversarial examplesInterpretability of deep neural networks for secure machine learningInterpretability of machine learning models for secure machine learning Adversarial examples in real-world applicationsAdversarial machine learningFederated learningPrivacy-preserving machine learning techniquesPrivacy-preserving learning in real-world applicationsImmune computation in secure learningEvolutionary computation in secure machine learningManuscript Preparation and SubmissionSubmitted manuscripts must not have been previously published or currently submitted for conference/journal publication elsewhere. The manuscripts should be prepared according to the ``Information for Authors" section of the journal found at: https://cis.ieee.org/publications/ieee-transactions-on-artificial-intelligence/information-for-authors-tai and submission should be done through the journal submission website: https://mc.manuscriptcentral.com/ tai-ieee. Follow the submission instructions given on this site; please select the article type as “SI: S&P in Machine Learning”.Guest EditorsWenjian Luo, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China, Email: luowenjian@hit.edu.cnYaochu Jin, Department of Computer Science, Univeristy of Surrey, UK, Email: yaochu.jin@surrey.ac.ukCatherine Huang, McAfee LLC, US, Email: Catherine_Huang@McAfee.com