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Project Information

CAREER: PRIVACY-PRESERVING LEARNING FOR DISTRIBUTED DATA

Agency:
NSF

National Science Foundation

Project Number:
1453432
Contact PI / Project Leader:
SARWATE, ANAND
Awardee Organization:
RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK

Description

Abstract Text:
Medical technologies such as imaging and sequencing make it possible to gather massive amounts of information at increasingly lower cost. Sharing data from studies can advance scientific understanding and improve healthcare outcomes. Concern about patient privacy, however, can preclude open data sharing, thus hampering progress in understanding stigmatized conditions such as mental health disorders. This research seeks to understand how to analyze and learn from sensitive data held at different sites (such as medical centers) in a way that quantifiably and rigorously protects the privacy of the data.

The framework used in this research is differential privacy, a recently-proposed model for measuring privacy risk in data sharing. Differentially private algorithms provide approximate (noisy) answers to protect sensitive data, involving a tradeoff between privacy and utility. This research studies how to combine private approximations from different sites to improve the overall quality or utility of the result. The main goals of this research are to understand the fundamental limits of private data sharing, to design algorithms for making private approximations and rules for combining them, and to understand the consequences of sites having more complex privacy and sharing restrictions. The methods used to address these problems are a mix of mathematical techniques from statistics, computer science, and electrical engineering.

The educational component of this research will involve designing introductory university courses and material on data science, undergraduate research projects, curricular materials for graduate courses, and outreach to the growing data-hacker community via presentations, tutorial materials, and open-source software.

The primary aim of this research is bridge the gap between theory and practice by developing algorithmic principles for practical privacy-preserving algorithms. These algorithms will be validated on neuroimaging data used to understand and diagnose mental health disorders. Implementing the results of this research will create a blueprint for building practical privacy-preserving learning for research in healthcare and other fields. The tradeoffs between privacy and utility in distributed systems lead naturally to more general questions of cost-benefit tradeoffs for learning problems, and the same algorithmic principles will shed light on information processing and machine learning in general distributed systems where messages may be noisy or corrupted.
Project Terms:
Address; Algorithm Design; Algorithms; career; Communities; Complex; computer science; Computer software; cost; Costs and Benefits; Data; design; Diagnosis; distributed data; Distributed Systems; Electrical Engineering; Goals; Healthcare; Image; improved; information processing; Lead; Learning; Light; Machine Learning; Measures; Medical center; Medical Technology; Mental disorders; Methods; Modeling; neuroimaging; open source; Outcome; outreach; patient privacy; Privacy; Research; Research Project Grants; research study; Risk; Science; Scientific Advances and Accomplishments; sharing data; Site; statistics; Techniques; theories; undergraduate research; Universities

Details

Contact PI / Project Leader Information:
Name:  SARWATE, ANAND
Other PI Information:
Not Applicable
Awardee Organization:
Name:  RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK
City:  NEW BRUNSWICK    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  06
Other Information:
Fiscal Year: 2015
Award Notice Date: 23-Jan-2015
DUNS Number: 001912864
Project Start Date: 01-Jul-2015
Budget Start Date:
CFDA Code: 47.070
Project End Date: 30-Jun-2020
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2015:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

FY Total Cost
2015 NSF

National Science Foundation

$215,865

Results

i

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