Trustworthy AI: Privacy-Preserving Techniques for a Secure Digital Future

Dear Colleagues,

The digital revolution has reshaped modern society and continues to advance at an unprecedented pace, permeating almost every facet of our lives. As we embrace the convenience and opportunities brought by digital technologies, the issue of privacy protection has emerged as a critical concern. Artificial intelligence (AI) and machine learning (ML), as core technologies driving this digital transformation, hold immense potential for innovation and progress. However, their rapid development and widespread application have also raised significant questions about privacy and data security. To address these challenges and ensure a positive digital future, there is a pressing need to develop privacy-preserving techniques in AI and ML that can safeguard sensitive information while enabling technological advancement. As digital technologies evolve, it is imperative to design new AI and ML schemes that protect sensitive data while enabling technological advancement.

This Special Issue "Trustworthy AI: Privacy-Preserving Techniques for a Secure Digital Future" aims to bring together researchers and practitioners from diverse fields to explore innovative methods and approaches for building trustworthy AI and ML systems that prioritize privacy. With the increasing reliance on software in various industries and aspects of daily life, the quality of software has become a critical factor in ensuring safety, security, and overall well-being. Poor software quality can lead to severe consequences, including safety hazards, security breaches, and financial losses. Therefore, managing software quality effectively is essential not only for minimizing risks but also for enhancing productivity, reducing costs, and accelerating time-to-market. The development of privacy-preserving techniques for AI and ML systems becomes even more vital. These techniques must be integrated into software development, analysis, and maintenance processes to ensure that AI and ML applications are secure, reliable, and trustworthy. By emphasizing privacy protection, we can build a more secure digital future where individuals and organizations can fully benefit from AI and ML technologies without compromising their privacy.

Distributed and decentralized machine learning have emerged as promising paradigms in the field of AI and ML. These approaches enable the training of models across multiple devices or nodes without the need for centralized data storage, thereby inherently supporting privacy preservation. By distributing the learning process, sensitive data can remain on local devices, reducing the risk of data breaches and unauthorized access. This makes distributed and decentralized learning particularly relevant to the theme of our special issue, as they offer innovative solutions to the challenges of privacy protection in AI and ML.

We cordially invite you to contribute your latest research findings and insights to this Special Issue. Topics of interest include but are not limited to:

1. Privacy-preserving AI and ML algorithms
2. Differential privacy in machine learning
3. Federated learning for data privacy
4. Secure multi-party computation in AI
5. Privacy-enhancing techniques for data collection and processing
6. Secure AI-driven robotics
7. Privacy-preserving energy systems (smart grids, renewable energy, power system optimization)
8. Trustworthy AI for healthcare applications
9. Context-aware privacy mechanisms for adaptive AI systems
10. Neuromorphic computing and privacy implications
11. Privacy in foundation models and multimodal LLMs
12. Cross-Layer Privacy Architecture for Heterogeneous Edge-Cloud AI Systems
13. Privacy-preserving hardware security
14. Differential privacy in computer vision
15. Federated learning for IoT devices
16. Privacy-aware environmental monitoring
17. Privacy-preserving healthcare applications (medical imaging, patient data)
18. Privacy-preserving pollution monitoring
19. Privacy-preserving supply chain optimization
20. Secure communication systems (private communications, signal transmission)
21. Secure nanotechnology applications
22. Privacy-preserving fire safety systems
23. Anonymization and de-identification methods
24. Privacy-aware model training and optimization
25. Ethical and legal considerations in AI/ML privacy
26. Privacy and security in AI-driven applications
27. Software quality assurance for privacy-preserving AI systems
28. Software development methodologies for secure AI
29. Testing and verification of privacy-preserving ML models
30. Software metrics for assessing privacy risks
31. Software refactoring for improved privacy protection
32. Debugging and fault localization in privacy-sensitive AI systems
33. Software evolution and maintenance for long-term privacy assurance
34. Software performance optimization in privacy-preserving settings
35. Dependable and secure AI computing infrastructures
36. Human-centric AI and privacy considerations
37. Digital identity management and privacy
38. Explainable AI and transparency in privacy decisions
39. Emerging trends and challenges in privacy-preserving AI and ML
40. Distributed and decentralized learning techniques (including privacy considerations)
41. Security and privacy in collaborative learning environments

Dr. Habib Ullah Manzoor
Dr. Sanaullah Manzoor
Dr. Ahsan Raza Khan
Guest Editors

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• privacy-preserving AI/ML
• differential privacy
• federated learning
• secure multi-party computation
• anonymization techniques
• ethical AI
• secure AI systems
• decentralized learning
• privacy in distributed systems
• trustworthy AI