Data Privacy: Compression & Protection

This lecture covers the challenges of managing sensitive data in the era of growing data volume and variety. It explores techniques like data perturbation, k-Anonymity, and Differential Privacy to ensure privacy while sharing information across private databases. The lecture also delves into privacy-preserving protocols for operations like intersection queries, randomized databases, and aggregate reconstruction, emphasizing the trade-off between privacy and data accuracy. Applications in data mining, such as training models on randomized data and building decision trees with privacy-preserving primitives, are discussed. The lecture concludes with insights on privacy guarantees and the privacy-error tradeoff in data sharing.