IEEE International Conference on Computer Communications
17–20 May 2023 // New York area // USA

The 2023 International Workshop on AI-Driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP 2023) - Call for Papers

The 2023 International Workshop on AI-Driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP 2023)  

Call for Papers


Artificial intelligence (AI), together with its learning techniques, including machine learning (ML), and deep learning (DL), represents a significant evolution in computer science and data processing that is rapidly changing many industries and profoundly influencing people’s daily lives. AI-driven solutions have already found applications in next-generation computing, communication, and decision-making on the networking paradigms, namely, the Internet of Things (IoT), Cyber-Physical Systems (CPS), Fog/Edge computing, Cloud computing, etc. Such solutions are vastly used in high-stakes applications like industrial automation, healthcare, automotive, energy, business, government, education, and justice, moving us toward a more algorithmic society.

Despite so many greatest benefits, unsecured or unreliable computation, privacy-violating data processing, communication, and inaccurate, faulty, unethical, unfair, or biased decisions in these platforms sometimes directly or indirectly cause harm to the application’s performance, its users, and society. Moreover, an individual’s privacy is significantly threatened by AI-based cyberattacks. The rise of AI-enabled cyberattacks could cause an explosion of networking penetration, personal data theft, malicious traffic data, and epidemic spread of intelligent malware in IoT/CPS/Cloud/Edge/Fog paradigms. Furthermore, AI-driven technologies require a training and testing process, introducing additional problems in securing and protecting training data and algorithms. Many ML/DL models are vulnerable against well-designed adversarial input samples. Distributed sharing data, outsourcing data, and associated algorithms for training require integrity and trustworthiness in the training stage. Furthermore, end-user data privacy and learning models must be protected. Thus, trustworthy, security, and privacy-preserving (TSP) computing in IoT/CPS/Cloud with AI-driven approaches is becoming an issue.

To that end, this workshop aims to provide a forum for researchers, engineers, and professionals across academia, government, and industries, to exchange ideas, present early results, and provide future visions concerning AI-driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP). Specific topics of interest include but are not limited to the following.

  • AI-driven TSP computing
  • AI-driven TSP computing in IoT/CPS/Edge/Fog/Cloud paradigms
  • AI-driven TSP computing smart data and Big data paradigm
  • AI-driven TSP computing with blockchain
  • AI-driven TSP computing in activity recognition, HCI
  • AI-driven TSP with wearable devices, embedded HW and SW, and applications
  • AI-driven TSP with agent-based computing
  • AI-driven TSP with nature- and brain-inspired computing
  • AI-driven TSP computing against AI-driven malware and fault injections
  • AI-driven TSP computing against AI-driven supply chain & hardware attacks
  • AI-driven TSP computing in big data capture, classification, and analytics
  • AI-driven TSP computing with nano & micro-systems and quantum computing
  • AI-driven TSP computing in OS, virtualization, database, and software systems
  • AI-driven TSP measures, metrics, verification, and validation
  • AI-driven sensing, detection, prevention, and recovery against potential threats
  • AI-driven applied cryptography and security protocols
  • AI-driven defense against AI-driven threats/attacks
  • AI-driven data trust, system trust, service trust, application trust, etc.
  • TSP with learning methods (ML/DL/DRL/FL)
  • TSP, anonymity, and resilience analysis on AI
  • TSP with AI-driven data mining and knowledge discovery
  • TSP concerns with AI-driven technologies, such as GAN
  • TSP with reactive distributed AI, AI tools, and applications
  • Trustworthy ML, DL, DRL, and FL methods and tools
  • Trustworthy AutoML, AutoDL, and automatic control
  • Trustworthiness with AI-driven authentication, access control, & monitoring
  • Theoretical studies on big data system trustworthiness, privacy, and security
  • Fairness, explainability, accountability, reliability, and safety with AI


Important Dates:

Submission Deadline: December 20, 2022 January 12, 2023 January 16, 2023

Notification of Acceptance: February 6, 2023

Camera Ready: March 6, 2023

Workshop: May 20, 2023



Submission link:


Questions can be directed to AidTSPresearch[AT]

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