Award Abstract # 1839868
CICI: RDP: Open Badge Researcher Credentials for Secure Access to Restricted and Sensitive Data

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date: August 23, 2018
Latest Amendment Date: September 27, 2018
Award Number: 1839868
Award Instrument: Standard Grant
Program Manager: Rob Beverly
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2018
End Date: August 31, 2021 (Estimated)
Total Intended Award Amount: $881,342.00
Total Awarded Amount to Date: $881,342.00
Funds Obligated to Date: FY 2018 = $881,342.00
History of Investigator:
  • Margaret Levenstein (Principal Investigator)
    maggiel@umich.edu
  • Libby Hemphill (Co-Principal Investigator)
  • Florian Schaub (Co-Principal Investigator)
  • Andrea Thomer (Co-Principal Investigator)
Recipient Sponsored Research Office: Regents of the University of Michigan - Ann Arbor
1109 GEDDES AVE STE 3300
ANN ARBOR
MI  US  48109-1015
(734)763-6438
Sponsor Congressional District: 06
Primary Place of Performance: University of Michigan Ann Arbor
MI  US  48106-1248
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): GNJ7BBP73WE9
Parent UEI:
NSF Program(s): Cybersecurity Innovation
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 802700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project reduces the complexity in protecting research data by developing and piloting an open badge research credential system (OBRCS). Open badges are visual tokens that signal achievement, affiliation, authorization, or another trust relationship and are shareable across the web. The challenges of managing and protecting restricted data mean that data providers are often wary of sharing sensitive data or that data ends up in the wrong hands, and potential gains to society and science from using those data go unrealized. OBRCS allows researchers to present their evolving credentials openly and to record their achievements and credentials publicly and enables more collaboration, facilitates data re-use, and supports replication efforts. OBRCS benefits the scientific community by ensuring the integrity, resilience, and reliability of research data.

Combining and analyzing collections of data enable scientific breakthroughs. Efficient, secure data sharing and reuse facilitates collaboration and replication, leading to better science. However, managing different access policies, authenticating, and authorizing access to sensitive data is a challenge faced by all data management organizations. Unauthorized access threatens the integrity of data and the privacy of study participants and these threats can impact the conclusions researchers draw. The project achieves these goals through three main activities: (a) develops an open badge system for managing researcher credentials, (b) articulates levels of data sensitivity and risk that indicate criteria for access, and (c) identifies the right balance between openness and privacy for data users in a restricted data access system.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Mhaidli, Abraham and Hemphill, Libby and Schaub, Florian and Jordan, Cundiff and Thomer, Andrea K. "Privacy Impact Assessments for Digital Repositories" International Journal of Digital Curation , v.15 , 1970 https://doi.org/10.2218/ijdc.v15i1.692 Citation Details
Tyler, Allison Rae "Facilitating Access to Restricted Data" International Journal of Digital Curation , v.15 , 2020 https://doi.org/10.2218/ijdc.v15i1.602 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

We developed a proof-of-concept system, the Researcher Passport, for capturing and storing information about researchers who request access to sensitive data. Researcher Passports are digital profiles that contain information about researchers? institutional affiliations, prior experience with restricted access data, and data security expertise. We created Researcher Passports and the application programming interfaces (APIs) necessary to create and share these profiles. These APIs will make it possible for data archives to verify researchers? credentials and identities when making decisions about access provision. Unauthorized access threatens the integrity of data and the privacy of study participants. The security and authority of Researcher Passports help minimize the risks of unauthorized data access while streamlining the application and review processes for researchers and data archives.

In designing and developing the Researcher Passport, we trained new teams of software developers to use ?privacy by design? and to construct and implement APIs. We built privacy ? for the researcher and for data subjects ? into the Researcher Passport so that using the system does not increase risks for researchers or expose their personal and professional data unnecessarily. ICPSR has adopted API structures broadly within their infrastructure, making the archive more flexible and extensible in the process.

We also conducted surveys with both researchers and individuals who may be represented in data contained in archives. Using data from these surveys, we are working to design matrices that explain the necessary credentials for working with particular datasets containing sensitive data (i.e., is the data covered by HIPAA? Must differential privacy techniques be employed?). These matrices will explain to both researchers and data subjects what protections are in place for a given dataset, increasing the transparency of restricted data access policies and procedures.

 


Last Modified: 12/21/2021
Modified by: Margaret Levenstein

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