2 citations · 5 across the 4 of their papers we have counts for
4 papers
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
Ritesh Ahuja, Sepanta Zeighami, Gabriel Ghinita +1
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many appl…
A Neural Database for Differentially Private Spatial Range Queries
Sepanta Zeighami, Ritesh Ahuja, Gabriel Ghinita +1
Mobile apps and location-based services generate large amounts of location data that can benefit research on traffic optimization, context-aware notifications and public health (e.…
HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data
Sina Shaham, Gabriel Ghinita, Ritesh Ahuja +2
Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical…
DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
Mingxuan Yue, Yaguang Li, Haoze Yang +3
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existi…