Brief Introduction

The rapid advancement of AI in Electronic Business (EB) has created a fundamental paradox: the hunger for massive data versus the imperative of privacy and security. This track serves as a multidisciplinary forum to bridge this gap by soliciting high-quality research on the technological foundations, economic incentives, and managerial strategies of privacy-preserving computing.

We explicitly invite contributions that explore the design, optimization, and implementation of secure AI systems—including Federated Learning (FL), Multi-party Computation (MPC), Differential Privacy (DP), and Trusted Execution Environments (TEE)—specifically tailored for e-commerce environments like personalized recommendation and intelligent risk management. Simultaneously, we encourage studies that investigate the socio-technical and economic impacts of these technologies, such as their role in fostering consumer trust, enabling cross-organizational data collaboration, and ensuring regulatory compliance (e.g., GDPR, PIPL). By integrating technical excellence with business strategic insight, this track aims to define how "Privacy-by-Design" can be transformed into a core competitive advantage in the AI-led EB landscape.

Topics

1. Technical Architectures for Secure EB: Novel designs of Federated Learning and decentralized AI frameworks for cross-platform e-commerce collaboration.
2. Privacy-Preserving Algorithms in Business: Implementation of Differential Privacy and Homomorphic Encryption in recommendation systems, targeted advertising, and fraud detection.
3. Security and Robustness of EB AI Models: Defense mechanisms against adversarial attacks, data poisoning, and model inversion in intelligent business systems.
4. Economic Incentives for Data Sharing: Game-theoretical models and mechanism designs for fair profit-sharing and data contribution in federated business networks.
5. Consumer Behavior and Trust: Empirical studies on how privacy-preserving technologies mitigate the "Privacy Paradox" and influence user adoption of AI services.
6. Governance and Regulatory Technology (RegTech): Technical frameworks and managerial policies for cross-border data flow and compliance in global e-commerce.
7. Socio-Technical System Design: The interplay between algorithmic fairness, transparency, and privacy-preserving constraints in automated decision-making.