Privacy Computing and User Data Protection Strategies in Social E-Commerce

This paper examines privacy computing technologies—including federated learning, secure multi-party computation (SMPC), and differential privacy—as solutions to balance data utility and privacy protection in social e-commerce. It highlights technical strategies such as decentralized modeling, noise-enhanced anonymization, and edge-based data processing to mitigate risks like centralized data breaches and cross-border compliance conflicts. Aligning with regulations such as China’s Personal Information Protection Law (PIPL) and the EU’s GDPR, the study proposes a tripartite framework integrating cryptographic techniques, dynamic user consent mechanisms, and regulatory adaptability. Empirical evidence demonstrates reduced privacy violations while maintaining operational efficiency, underscoring the necessity of verifiable privacy-preserving methods for sustainable social e-commerce development.