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A federated learning optimization algorithm integrating personalized differential privacy with verifiable secret sharing for enterprise digital transformation

computer-networks-and-communications
security-and-privacy
world-wide-web-and-web-science
neural-networks

Abstract

This paper proposes PDP-VSS, an enterprise-oriented federated learning framework that synergistically integrates Personalized Differential Privacy (PDP) and Verifiable Secret Sharing (VSS) to address critical security and privacy challenges in digital transformation initiatives. Our framework introduces three key innovations: (1)The framework dynamically allocates customized privacy budgets (e) to participating clients based on their data sensitivity and risk profiles, implementing adaptive Gaussian noise injection during local model updates. (2) We incorporate a threshold-based VSS mechanism to partition and distribute model parameters across multiple servers, ensuring robustness against malicious attacks while maintaining computational efficiency. Experimental evaluation on financial fraud detection (Credit Card Dataset) and commercial analytics (Bank Marketing Dataset) demonstrates our method achieves 92.9% average accuracy (1.9% improvement over baseline FL) with 19.3% reduced communication overhead. Security analysis confirms the framework’s resilience against membership inference attacks (success rate ¡29%) and gradient leakage threats. (3)The proposed PDP-VSS integration establishes a new paradigm for privacy-preserving distributed learning in enterprise environments, particularly valuable for cross-departmental collaborations requiring compliance with stringent data protection regulations.
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