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Spotlight
Wed 13:58 Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer, Dan Boneh
Poster
Thu 1:00 Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu
Poster
Thu 9:00 CaPC Learning: Confidential and Private Collaborative Learning
Christopher Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, Xiao Wang
Poster
Thu 9:00 Differentially Private Learning Needs Better Features (or Much More Data)
Florian Tramer, Dan Boneh
Workshop
Fri 9:10 Frequency Estimation in Local and Multiparty Differential Privacy
Graham Cormode
Workshop
Fri 9:35 Q&A for Frequency Estimation in Local and Multiparty Differential Privacy
Workshop
Fri 9:40 Inference Risks for Machine Learning
David Evans
Workshop
Fri 12:15 A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
Lalitha Sankar
Workshop
Differentially Private Multi-Task Learning
Shengyuan Hu, Steven Wu, Virginia Smith
Workshop
Distributed Gaussian Differential Privacy Via Shuffling
Kan Chen, Qi Long
Workshop
CAUSALLY CONSTRAINED DATA SYNTHESIS FOR PRIVATE DATA RELEASE
Varun Chandrasekaran, Darren Edge, Somesh Jha, Amit Sharma, Cheng Zhang, Shruti Tople
Workshop
Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT
Antti Koskela, Antti Honkela
Workshop
UNDERSTANDING CLIPPED FEDAVG: CONVERGENCE AND CLIENT-LEVEL DIFFERENTIAL PRIVACY
Xinwei Zhang, Xiangyi Chen, Jinfeng Yi, Steven Wu, Mingyi Hong
Workshop
Does Differential Privacy Defeat Data Poisoning?
Matthew Jagielski, Alina Oprea
Workshop
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin Inan, Marcello Hasegawa, Robert Sim
Workshop
Towards Prior-Free Approximately Truthful One-Shot Auction Learning via Differential Privacy
Daniel Reusche, Nicolás Della Penna