Assuring privacy of AI-powered community driven Android code vulnerability detection
Conference paper
Senanayake, J., Kalutarage, H., Piras, L., Al-Kadri, M.O. and Petrovski, A. 2024. Assuring privacy of AI-powered community driven Android code vulnerability detection. 3rd International Workshop on System Security Assurance. Bydgoszcz, Poland 19 - 20 Sep 2024 Springer.
Type | Conference paper |
---|---|
Title | Assuring privacy of AI-powered community driven Android code vulnerability detection |
Authors | Senanayake, J., Kalutarage, H., Piras, L., Al-Kadri, M.O. and Petrovski, A. |
Abstract | The challenge of training AI models is heightened by the limited availability of data, particularly when public datasets are insufficient. |
Keywords | Android code vulnerability; federated learning; differential privacy; data scarcity; artificial intelligence |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Research Group | MDX Software Engineering, Theory & Algorithms (SETA) Reseach Group |
Conference | 3rd International Workshop on System Security Assurance |
ISSN | 0302-9743 |
Electronic | 1611-3349 |
Publisher | Springer |
Publication process dates | |
Accepted | 21 Jul 2024 |
Deposited | 09 Oct 2024 |
Output status | Accepted |
Accepted author manuscript | File Access Level Open |
Copyright Statement | This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/[insert DOI]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-ma... |
Language | English |
https://repository.mdx.ac.uk/item/1qz83x
Restricted files
Accepted author manuscript
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