Secure Preference Learning for Social E-Commerce: Integrating Adversarial Robustness, Differential Privacy, and Interpretable User-Intent Modeling
Keywords:
social e-commerce, preference learning, adversarial robustness, differential privacy, interpretable machine learning, federated learning, privacy-utility trade-offAbstract
The convergence of social media and e-commerce has given rise to social e-commerce platforms where user engagement, content sharing, and transaction behaviors are deeply intertwined. Personalization systems on these platforms rely on learning user preferences from rich, high-dimensional interaction data. However, such systems face critical challenges related to data privacy, adversarial manipulation, and the opacity of learned models. This paper presents a comprehensive framework for secure preference learning in social e-commerce that integrates three complementary pillars: adversarial robustness, differential privacy, and interpretable user-intent modeling. We examine the structural trade-offs between privacy guarantees, model accuracy, and resilience to malicious inputs, and propose a socio-technical architecture that balances these objectives at the system level. The framework leverages federated learning to distribute model training across user devices, thereby reducing central data aggregation risks. Differential privacy mechanisms are embedded at both local and global levels to limit information leakage from user data and model updates. Adversarial training and input sanitization strategies are employed to defend against poisoning attacks and gradient manipulation. Interpretability is addressed through attention-based intent attribution and counterfactual explanations that provide actionable insights for platform stakeholders. We analyze governance considerations, including fairness across demographic groups, regulatory compliance, and the sustainability of privacy budgets over long-term deployment. Cross-domain comparisons with traditional recommendation systems and collaborative filtering approaches are drawn to highlight the unique challenges of social e-commerce contexts. The paper concludes by outlining future directions for privacy-preserving, transparent, and robust preference learning in dynamic social commerce environments.
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