HTF: Homogeneous Tree Framework for Differentially-Private Release of Large Geospatial Datasets with Self-Tuning Structure Height.

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 queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. In addition, they use ad-hoc criteria to decide the depth of the partitioning. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We propose a self-tuning approach to decide the depth of the partitioning structure that optimizes the use of privacy budget. Furthermore, we provide an optimization that scales the proposed split approach to large datasets while maintaining accuracy. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.

Reimagining the IR Workflow for a Better Work-Life Balance.

Several workflow changes were implemented in a large academic interventional radiology practice, including separation of inpatient and outpatient services, early start times, and using an adaptive learning system to predict case length tailored to individual physicians. Metrics including procedural volume, on-time start, accuracy at predicting case length, and room shutdown time were assessed before and after the intervention. Considerable improvements were seen in accuracy of first case start times, predicting block times, and last case encounter ending times. It is proposed that with improved role clarity, interventional radiologists can regain control over their schedules, utilize work hours more efficiently, and improve work-life balance.