Assured Information Management and Sharing (AIMS)

Department of Computer Science
Emory University

Research Statement
Enabling trustworthy and privacy-preserving data sharing is key to addressing critical issues as diverse as healthcare and national security. The AIMS research lab conducts research in the intersection of big data, machine learning, and information privacy and security aimed at developing algorithms and techniques for enhancing privacy and robustness of data driven systems.

Current Projects
REACT: Real-time contact tracing and risk monitoring via privacy enhanced mobile tracking (NSF RAPID) (with Vicki Hertzberg and Lance Waller at Emory, Cyrus Shahabi at USC, and Xiaoqian Jiang and Amy Franklin at UTHealth)

TIMES: A tensor factorization platform for spatio-temporal data (NSF BigData) (PI: Joyce Ho at Emory, GaTech: Prof. Jimeng Sun)

Decentralized differentially-private methods for dynamic data release and analysis (NIH R01) (PI: Lucila Ohno-Machado at UCSD, UTHealth: Xiaoqian Jiang)

Spatiotemporal Privacy for Location Based Applications (NSF SaTC)

Next Generation Frameworks for Secure DDDAS/Infosymbiotics Systems (AFOSR DDDAS) (with Vaidy Sunderam at Emory)

Selected Past Projects
Building Patient-Centered Privacy Preserving Data Registries (PCORI) (with Prof. Andrew Post at Emory, Prof. Xiaoqian Jiang at UTHealth, and Prof. Lucila Ohno-Machado at UCSD)
SHARE: Statistical Health Information Release with Differential Privacy (NIH R01) (with Prof. Andrew Post at Emory, Prof. Xiaoqian Jiang at UTHealth, and Prof. Lucila Ohno-Machado at UCSD)
Adaptive Differentially Private Data Release (NSF SaTC)
I-Corps: iCloak: Privacy Preserving Individual Location Sharing (NSF I-Corps)
Extending Differential Privacy for Privacy Preserving Location Sharing (Google Research Award)
PREDICT: PRivacy Enhancing Dynamic Information Collection and moniToring (AFOSR DDDAS) (with Prof. Vaidy Sunderam at Emory)
HIDE™: Health Information DE-identification (Woodrow Wilson Foundation)
Enabling Privacy for Data Federation Services (Cisco Research Award)
FRIL: Fine-grained Record Integration and Linkage (CDC) 

Acknowledgement
We acknolwedge the generous support by NSF, AFOSR, NIH, and PCORI, Google Research Award, Woodrow Wilson Foundation, Cisco research award, IBM Faculty Innovation Award, and various support from Emory University (Math/CS, College, URC, and CCI).

Any opinions, findings, and conclusions or recommendations expressed in the project material are those of the authors and do not necessarily reflect the views of the sponsors.