Personalized differential privacy for high-dimensional data: A random sampling and pruning privacy tree approach

Summary

This research paper proposes a personalized differential privacy approach for publishing high-dimensional data, combining random sampling and a pruning privacy tree. The source text is an introduction that motivates the work by arguing that traditional anonymization is weak against inference attacks and that uniform privacy budgets fail to meet heterogeneous privacy needs. It reviews prior personalized differential privacy mechanisms, including Jorgensen et al. and Alaggan et al.