MO4.R3.2

Differentially Private Synthetic Data with Private Density Estimation

Nikolija Bojkovic, University of Belgrade, Serbia; Po-Ling Loh, University of Cambridge, United Kingdom
Session:
Differential Privacy in Learning 2
Track:
16: Privacy and Fairness
Location:
Ypsilon IV-V-VI
Presentation Time:
Mon, 8 Jul, 16:45 - 17:05
Session Chair:
Ayfer Ozgur, Stanford University
Presentation
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Discussion
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Session MO4.R3
MO4.R3.1: Optimal Private Discrete Distribution Estimation with One-bit Communication
Seung-Hyun Nam, KAIST, Korea (South); Vincent Y. F. Tan, National University of Singapore, Singapore; Si-Hyeon Lee, KAIST, Korea (South)
MO4.R3.2: Differentially Private Synthetic Data with Private Density Estimation
Nikolija Bojkovic, University of Belgrade, Serbia; Po-Ling Loh, University of Cambridge, United Kingdom
MO4.R3.3: Training Generative Models from Privatized Data via Entropic Optimal Transport
Daria Reshetova, Wei-Ning Chen, Ayfer Ozgur, Stanford University, United States
MO4.R3.4: Differentially Private Fair Binary Classifications
Hrad Ghoukasian, Shahab Asoodeh, McMaster University, Canada
Resources
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