CONSISTENT SPECTRAL CLUSTERING OF NETWORK BLOCK MODELS UNDER LOCAL DIFFERENTIAL PRIVACY.

The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of spectral clustering of SBM and DCBM networks under a local form of edge differential privacy. Using a randomized response privacy mechanism called the edge-flip mechanism, we develop theoretical guarantees for differentially private community detection, demonstrating conditions under which this strong privacy guarantee can be upheld while achieving spectral clustering convergence rates that match the known rates without privacy.

Automatable Distributed Regression Analysis of Vertically Partitioned Data Facilitated by PopMedNet: Feasibility and Enhancement Study.

Background: In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression-a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information-with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources.

Child Migrant Health: The Role of Pediatric Surgery in the Migrants' Route across Balkan Countries to North-West Europe.

Introduction:  Child migrants are the most vulnerable population, prone to various health conditions due to trauma and the bad living conditions that they experience during their migration. The objective of this study was to determine the pediatric surgical conditions of migrant children treated in our hospital on their way toward North-West Europe.

Materials And Methods:  A retrospective analysis was performed on all admitted migrants in one tertiary and one secondary level hospital from 2016 to 2018.

Statistical models for incorporating data from routine HIV testing of pregnant women at antenatal clinics into HIV/AIDS epidemic estimates.

Objective: HIV prevalence data collected from routine HIV testing of pregnant women at antenatal clinics (ANC-RT) are potentially available from all facilities that offer testing services to pregnant women and can be used to improve estimates of national and subnational HIV prevalence trends. We develop methods to incorporate these new data source into the Joint United Nations Programme on AIDS Estimation and Projection Package in Spectrum 2017.

Methods: We develop a new statistical model for incorporating ANC-RT HIV prevalence data, aggregated either to the health facility level (site-level) or regionally (census-level), to estimate HIV prevalence alongside existing sources of HIV prevalence data from ANC unlinked anonymous testing (ANC-UAT) and household-based national population surveys.