Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI

Dilli Prasad Sharma (), Arash Habibi Lashkari (), Mahdi Daghmehchi Firoozjaei (), Samaneh Mahdavifar () and Pulei Xiong ()
Additional contact information
Dilli Prasad Sharma: University of Toronto
Arash Habibi Lashkari: York University
Mahdi Daghmehchi Firoozjaei: MacEwan University
Samaneh Mahdavifar: McGill University
Pulei Xiong: National Research Council of Canada

Chapter Chapter 9 in Understanding AI in Cybersecurity and Secure AI, 2025, pp 159-195 from Springer

Abstract: Abstract This chapter presents adversarial defense strategies and privacy-preserving technologies for securing AI systems. It begins with discussing various defense methods against adversarial attacks and privacy-preserving techniques, including adversarial training, certified robustness, gradient masking, adversarial detection, data anonymization, homomorphic encryption, federated learning, and secure multi-party computations. It also explores the importance of model robustness, privacy preservation, explainability, and safe deployment. Additionally, this chapter examines explainable AI (XAI) methods, including SHAP, LIME, and feature attribution, to improve model interpretability. Finally, it discusses the challenges of applying these defense methods in real-world AI deployments for secure and transparent AI systems.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-91524-6_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031915246

DOI: 10.1007/978-3-031-91524-6_9

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().