A Framework for Privacy-Preserving Data Analytics Using Differential Privacy

Abstract

This paper presents a comprehensive privacy-preserving analytics framework that embeds differential privacy principles across the entire data lifecycle, from collection and preprocessing to analysis, interaction, and output generation. Unlike approaches that simply apply existing differential privacy libraries, the proposed platform emphasizes end-to-end integration, ensuring that privacy guarantees are continuously maintained throughout the analytical process. An adaptive privacy management layer dynamically regulates privacy budgets and mechanisms based on data sensitivity, analytical objectives, and real-time system conditions, enabling a balanced trade-off between privacy protection and analytical utility. The framework also supports real-time privacy-preserving analytics, demonstrating that strong confidentiality measures can coexist with responsive and practical data-driven decision-making. A proof-of-concept prototype demonstrates the architecture's feasibility and illustrates its applicability to high-sensitivity domains that require rigorous privacy assurances.