Differential Privacy at Scale for Data Lakes

Abstract

Data lakes, with their capacity to store vast amounts of raw and diverse data, are increasingly central to enterprise analytics. However, preserving privacy in such large-scale environments presents significant challenges. Differential privacy (DP) offers mathematically rigorous guarantees, but applying it at scale in data lakes involves confronting issues like massive data volumes, unknown data domains, complex query workloads, and shared privacy budgets. This paper explores methods and systems aimed at implementing differential privacy at scale within data lakes, focusing on prior-to-2022 solutions. We examine Plume—a system designed by Google to handle privacy across trillions of records, addressing multiple records per user, undefined domains, and scalability of private aggregation pipelines arXiv+1. We also survey LinkedIn’s DP analytics API integrating DP into real-time analytics and enforcing user-level budget management USENIX. Our literature review highlights core concepts: privacy budgeting, system-level enforcement, noise injection mechanisms, and the practical challenges inherent in distributing privacy across diverse workloads. Research methodology includes reviewing academic and industrial case studies and extracting architectural and operational best practices for DP’s deployment over data lake environments. Advantages include strong theoretical privacy, adaptability to large datasets, and support for real-time analytics. Disadvantages center on complexity in budget management, trade-offs between utility and privacy, computational overhead, and the need for robust system integration. We conclude that differential privacy is feasible at the scale of large data lakes provided architectural attention is given to privacy budget coordination, utility preservation, and performance optimization. Future directions include AIassisted privacy budget tuning, DP integration with data governance workflows, and support for streaming analytics within DP-enforced environments.