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Marco Gaboardi

Boston University

Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy

Nov 11, 2020
Di Wang, Marco Gaboardi, Adam Smith, Jinhui Xu

* Appeared at Journal of Machine Learning Research. The journal version of arXiv:1802.04085, fixed a bug in arXiv:1812.06825 

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Controlling Privacy Loss in Survey Sampling (Working Paper)

Jul 24, 2020
Mark Bun, Jörg Drechsler, Marco Gaboardi, Audra McMillan

* Working paper, 7 pages 

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Facility Location Problem in Differential Privacy Model Revisited

Oct 26, 2019
Yunus Esencayi, Marco Gaboardi, Shi Li, Di Wang

* Accepted to NeurIPS 2019 

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Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data

Oct 01, 2019
Di Wang, Huanyu Zhang, Marco Gaboardi, Jinhui Xu


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Privacy Amplification by Mixing and Diffusion Mechanisms

May 29, 2019
Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek


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Hypothesis Testing Interpretations and Renyi Differential Privacy

May 24, 2019
Borja Balle, Gilles Barthe, Marco Gaboardi, Justin Hsu, Tetsuya Sato


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Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Jul 04, 2018
Borja Balle, Gilles Barthe, Marco Gaboardi


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Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case

May 16, 2018
Di Wang, Marco Gaboardi, Jinhui Xu

* Add a new section on high dimensional case 

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Dual Query: Practical Private Query Release for High Dimensional Data

Nov 19, 2015
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

* Journal of Privacy and Confidentiality 7(2) 53--77 (2017) 

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