Differential Privacy Mechanisms for Large-Scale Enterprise Data Platforms

Abstract

Large-scale enterprise data platforms have exacerbated data analytics-security conflicts. Traditional anonymization and access control fail to protect privacy under frequent inquiries or sophisticated inference attacks. The possibility of enterprise-scale data ecosystem differential privacy strategies to give mathematically defined privacy protections while preserving analytic utility is studied. Research examines noise injection, privacy budget allocation, query response techniques, and scalable deployment architecture. The work addresses previous methods' re-identification and data leakage concerns by incorporating differential privacy into commercial data operations. Contributions include an analytical analysis of privacy preservation-utility trade-offs, context-aware privacy parameterization suggestions, and a performance assessment across corporate datasets. Complex organizational settings may contain significant privacy controls, impacting regulatory compliance, risk mitigation, and privacy-conscious analytics architecture design.