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.
TypeConference paper
TitleAssuring privacy of AI-powered community driven Android code vulnerability detection
AuthorsSenanayake, 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.
While obtaining data from private sources may seem like a viable solution, privacy concerns often prevent data sharing. Therefore, it is essential to establish a system that effectively balances privacy concerns with the need for data. In our previous work, we introduced “Defendroid”, which focuses on real-time Android code vulnerability detection using a blockchain federated neural network with explainable artificial intelligence. In this study, the Defendroid approach is enhanced by incorporating variable differential privacy techniques to ensure the privacy of the model training process. The proposed method significantly improves privacy, achieving a privacy budget between 1 and 1.5, while maintaining Defendroid's baseline accuracy of 96% and an F1-Score of 0.96. As a result, this research thoroughly addresses concerns about the privacy of source code, filling a critical gap. This advancement not only showcases the effectiveness of the new approach but also its capability to address the significant challenges of privacy and data scarcity in AI-driven, community-focused Android code vulnerability detection.

KeywordsAndroid code vulnerability; federated learning; differential privacy; data scarcity; artificial intelligence
Sustainable Development Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
Research GroupMDX Software Engineering, Theory & Algorithms (SETA) Reseach Group
Conference3rd International Workshop on System Security Assurance
ISSN0302-9743
Electronic1611-3349
PublisherSpringer
Publication process dates
Accepted21 Jul 2024
Deposited09 Oct 2024
Output statusAccepted
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...

LanguageEnglish
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