Dynamic Noise Injection with On-Demand Bootstrapping (DNIB): A Robust Framework for Privacy-Preserving Data Analysis

Balancing the protection of sensitive data with maintaining its utility for analysis remains a pressing challenge in industries such as healthcare, finance, and government, where large-scale data processing is integral. Existing methods for privacy preservation often suffer from limitations, either introducing excessive constraints on data usability or failing to adequately secure sensitive information. This research addresses these issues by proposing Dynamic Noise Injection with On-Demand Bootstrapping (DNIB), a novel, multi-layered framework designed to dynamically safeguard data privacy while maintaining analytical accuracy. DNIB combines three core techniques-Differential Privacy for noise injection, Just-In-Time Processing to minimize unnecessary data exposure, and Bootstrap Resampling to enable secure analysis using synthetic datasets. The framework's layered architecture provides real-time privacy adjustments based on data sensitivity, supports secure data access, and generates privacy-preserving outputs. Experimental results demonstrate DNIB's effectiveness in significantly reducing data leakage risks while achieving high analytical utility. This research establishes DNIB as a transformative solution for adaptive, real-time data protection, with applicability across domains requiring stringent privacy measures.