Locally Differentially Private Sparse Vector Aggregation

Locally Differentially Private Sparse Vector Aggregation

Explore a concise 18-minute IEEE conference talk on locally differentially private sparse vector aggregation. Gain insights from experts Mingxun Zhou, Tianhao Wang, T-H. Hubert Chan, Giulia Fanti, and Elaine Shi as they discuss advanced techniques for preserving privacy in data aggregation. Learn about the challenges and solutions in handling sparse vectors while maintaining local differential privacy, a crucial aspect of modern data analysis and machine learning applications.