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Privacy disparity? Addressing privacy concerns in health disparity research

Background: Most research on identifying and understanding health disparities focused on measurement strategies and analytics design over administrative and survey data. What has received less attention, however, is the complex...

Polishing Up Data Privacy Protections

Nan Zhang, co-director of the Robust Analytics Lab (RAL), was recently featured in Kogod News discussing the implications of data protection regulations like the European Union’s General Data Protection Regulation...

Heng Xu won the Operational Research Society’s Stafford Beer Medal for 2018

Heng Xu was recently awarded The Operational Research Society’s Stafford Beer Medal for 2018 for her paper:

SIGMOD 2019 Paper: Compact Data Representation with Bounded Rank Regret

Selecting the best items in a dataset is a common task in data exploration. However, the concept of “best” lies in the eyes of the beholder - different users may...

Key to Security and Privacy

Heng Xu, co-director of the Robust Analytics Lab (RAL), was recently featured in Kogod News discussing why cyber risk awareness is so essential, yet often lacking, for preventing data breaches....

AU Ed Talk: Data in the everyday

Heng Xu, co-director of the Robust Analytics Lab, will deliver one of the four AU Ed Talks in this year’s All American Weekend. Her talk, “Data in the Everyday”, will...

Privacy-Preserving Framework for Publishing Electronic Healthcare Records

In the 2018 Connections in Smart Health Workshop, Nan Zhang and collaborators presented a poster on their recent progress for an NSF-sponsored collaborative research project on “Privacy-Preserving Framework for Publishing...

Talk at UMD: Privacy Contextualization: the Good, the Bad, and the Ugly

Heng Xu, co-director of the Robust Analytics Lab, will give a talk at the iSchool, University of Maryland, College Park, titled “Privacy Contextualization: the Good, the Bad, and the Ugly”....

NSF Project: Robust Twitter Analytics

We gratefully acknowledge the National Science Foundation for their support on our project 1850605, “RR: Establishing and Boosting Confidence Levels for Empirical Research Using Twitter Data”.