MO3.R3.3

Utilitarian Privacy and Private Sampling

Aman Bansal, Stanford University, United States; Rahul Chunduru, University of Wisconsin, Madison, United States; Deepesh Data, University of California, Los Angeles, United States; Manoj Prabhakaran, Indian Institute of Technology Bombay, India
Session:
Differential Privacy in Learning 1
Track:
16: Privacy and Fairness
Location:
Ypsilon IV-V-VI
Presentation Time:
Mon, 8 Jul, 15:15 - 15:35
Session Chair:
Oliver Kosut, Arizona State University
Presentation
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Discussion
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Session MO3.R3
MO3.R3.1: On the Privacy Guarantees of Differentially Private Stochastic Gradient Descent
Shahab Asoodeh, McMaster University, Canada; Mario Diaz, Universidad Nacional Autónoma de México, Mexico
MO3.R3.2: Controlled privacy leakage propagation throughout differential private overlapping grouped learning
Shahrzad Kiani, University of Toronto, Canada; Franziska Boenisch, CISPA, Germany; Stark C. Draper, University of Toronto, Canada
MO3.R3.3: Utilitarian Privacy and Private Sampling
Aman Bansal, Stanford University, United States; Rahul Chunduru, University of Wisconsin, Madison, United States; Deepesh Data, University of California, Los Angeles, United States; Manoj Prabhakaran, Indian Institute of Technology Bombay, India
MO3.R3.4: Age Aware Scheduling for Differentially-Private Federated Learning
Kuan-Yu Lin, National Yang Ming Chiao Tung University, Taiwan; Hsuan-Yin Lin, Simula UiB, Norway; Yu-Pin Hsu, National Taipei University, Taiwan; Yu-Chih Huang, National Yang Ming Chiao Tung University, Taiwan
Resources
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