Secure Data Processing Frameworks for Regulatory-Compliant Analytics under GDPR and CCPA

Abstract

Secure data processing for GDPR and CCPA compliance analytics without decreasing computational efficiency is needed. For varied and large datasets, current methods are computationally expensive, analytically imprecise, and unscalable. This research introduces a safe data processing system with privacy-preserving methods, access control, and cryptographic protocols for regulatory compliance and analytical performance. Modular data intake pipelines, fine-grained anonymization, secure multi-party computing, and differential privacy models allow compliant, performant analytics. Building enterprise-grade scalable systems, formalizing compliance-aware data processing, and analyzing privacy and analytical accuracy trade-offs are contributions. In sensitive data-dependent organizations, regulatory compliance and data analytics should be integrated to encourage responsible data use and informed decision-making.