Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43664, first published .
Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks

Alexander Shen   1, 2 , MSc ;   Luke Francisco   1 , BA ;   Srijan Sen   3, 4 , MD, PhD ;   Ambuj Tewari   1, 5 , PhD

1 Department of Statistics, University of Michigan, Ann Arbor, MI, United States

2 Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, United States

3 Eisenberg Family Depression Center, University of Michigan, Ann Arbor, MI, United States

4 Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States

5 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

Corresponding Author:

  • Alexander Shen, MSc
  • Department of Statistics and Data Science
  • Carnegie Mellon University
  • 5000 Forbes Ave
  • Pittsburgh, PA, 15213
  • United States
  • Phone: 1 7022754242
  • Email: alexshen@umich.edu