Joint Mathematics Meetings AMS Special Session

Current as of Thursday, April 21, 2022 04:01:57

• Program
• ·
• Deadlines
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• Timetable
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• Inquiries: meet@ams.org

Virtual Joint Mathematics Meetings (formerly in Seattle, WA)

• now meeting virtually, PT (hosted by the American Mathematical Society), Seattle, WA
• April 6-9, 2022 (Wednesday - Saturday)
• Meeting #1174

Associate Secretary for the AMS Scientific Program:

Georgia Benkart, University of Wisconsin-Madison benkart@math.wisc.edu

AMS Special Session on Advancing Data Privacy-Preserving Methodologies

• Wednesday April 6, 2022, 8:00 a.m.-11:30 a.m.
  AMS Special Session on Advancing Data Privacy-Preserving Methodologies
  Organizers:
  Claire McKay Bowen, Urban Institute
  

  • 8:00 a.m.
    Introduction on Data Privacy and Confidentiality: Protecting Your Privacy in a Data-Driven World
    Claire McKay Bowen*, Urban Institute
    (1174-62-5467)
  • 8:30 a.m.
    Differentially private methods for managing model uncertainty in linear regression models
    Andres Felipe Barrientos*, Department of Statistics, Florida State University
    Víctor Peña, Baruch College, The City University of New York
    (1174-62-8913)
  • 9:30 a.m.
    Problems on Random Graphs under Local Differential Privacy
    Jonathan Hehir*, Penn State University
    Xiaoyue Niu, Penn State University
    Aleksandra Slavkovic, Penn State University
    Siddharth Vishwanath, Penn State University
    (1174-62-9883)
  • 10:00 a.m.
    Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism
    Matthew Reimherr, Penn State University
    Jeremy Seeman*, Penn State University
    Aleksandra Slavkovic, Penn State University
    (1174-60-6361)
  • 11:00 a.m.
    Differentially Private machine learning algorithms for data sharing
    Ellen Galantucci*, Bureau of Labor Statistics
    Alex Measure, Bureau of Labor Statistics
    David Oh, Bureau of Labor Statistics
    (1174-62-5994)
• Wednesday April 6, 2022, 1:00 p.m.-4:00 p.m.
  AMS Special Session on Advancing Data Privacy-Preserving Methodologies, II
  Organizers:
  Claire McKay Bowen, Urban Institute
  

  • 1:00 p.m.
    Private Tabular Survey Data Products through Synthetic Microdata Generation
    Jingchen Hu*, Vassar College
    Terrance Savitsky, Bureau of Labor Statistics
    Matthew R Williams, National Center for Science and Engineering Statistics
    (1174-62-10060)
  • 1:30 p.m.
    Differentially private synthetic data for CDC WONDER
    Harrison Quick*, Drexel University
    (1174-68-6423)
  • 2:30 p.m.
    Differentially Private Generation of Social Networks
    Claire McKay Bowen, Urban Institute
    Evercita Cuevas Eugenio*, Sandia National Laboratories
    Ick Hoon Jin, Yonsei University
    Fang Liu, University of Notre Dame
    (1174-62-8978)
  • 3:30 p.m.
    Statistical Data Privacy: Where Do We Go from Here?
    Joshua Snoke*, RAND Corporation
    (1174-62-9020)