Spotlights
in
Workshop: Privacy Preserving Machine Learning

Spotlight talks

2018 Spotlights
in
Workshop: Privacy Preserving Machine Learning

Abstract

[Cynthia Dwork and Vitaly Feldman] Privacy-preserving Prediction (#05)
[Garrett Bernstein and Daniel Sheldon] Differentially Private Bayesian Inference for Exponential Families (#03)
[Di Wang, Adam Smith and Jinhui Xu] High Dimensional Sparse Linear Regression under Local Differential Privacy: Power and Limitations (#11)
[Ashwin Machanavajjhala and Kamalika Chaudhuri] Capacity Bounded Differential Privacy (#25)
[Aurélien Bellet, Rachid Guerraoui and Hadrien Hendrikx] Who started this gossip? Differentially private rumor spreading (#26)
[Antti Koskela and Antti Honkela] Learning rate adaptation for differentially private stochastic gradient descent (#38)
[Kareem Amin, Travis Dick, Alex Kulesza, Andres Medina and Sergei Vassilvitskii] Private Covariance Estimation via Iterative Eigenvector Sampling (#45)
[Kareem Amin, Alex Kulesza, Andres Munoz Medina and Sergei Vassilvitskii] Bias Variance Trade-off in Differential Privacy (#53)
[Nicolas Loizou, Peter Richtarik, Filip Hanzely, Jakub Konecny and Dmitry Grishchenko] A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion (#57)
[Brendan McMahan and Galen Andrew] A General Approach to Adding Differential Privacy to Iterative Training Procedures (#62)
[Da Yu, Huishuai Zhang and Wei Chen] Improving the Gradient Perturbation Approach for Differentially Private Optimization (#70)
[Alexandra Schofield, Aaron Schein, Zhiwei Steven Wu and Hanna Wallach] A Variational Inference Approach for Locally PrivateInference of Poisson Factorization Models (#63)
[Judy Hoffman, Mehryar Mohri and Ningshan Zhang] Algorithms and Theory for Multiple-Source Adaptation (#52)
[Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues and Guillermo Sapiro] Learning Representations for Utility and Privacy: An Information-Theoretic Based Approach (#15)
[Fabrice Benhamouda and Marc Joye] How to Profile Privacy-Conscious Users in Recommender Systems (#32)
[Koen Lennart van der Veen, Ruben Seggers, Peter Bloem and Giorgio Patrini] Three Tools for Practical Differential Privacy (#29)
[Vasyl Pihur, Aleksandra Korolova, Frederick Liu, Subhash Sankuratripati, Moti Yung, Dachuan Huang and Ruogu Zeng] Differentially Private "Draw and Discard" Machine Learning (#60)
[Hsin-Pai Cheng, Patrick Yu, Haojing Hu, Feng Yan, Shiyu Li, Hai Li and Yiran Chen] LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning (#01)
[Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko and Sergey Yekhanin] An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors (#12)
[Antoine Boutet, Théo Jourdan and Carole Frindel] Toward privacy in IoT mobile devices for activity recognition (#13)
[Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi and Todd Mytkowicz] CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs (#14)
[Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert and Jonathan Passerat-Palmbach] A generic framework for privacy preserving deep learning (#43)
[Valerie Chen, Valerio Pastro and Mariana Raykova] Secure Computation for Machine Learning With SPDZ (#44)
[Phillipp Schoppmann, Adria Gascon, Mariana Raykova and Benny Pinkas] Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning (#51)
[Siddharth Garg, Zahra Ghodsi, Carmit Hazay, Yuval Ishai, Antonio Mercedone and Muthuramakrishnan Venkitasubramaniam] Oursourcing Private Machine Learning via Lightweight Secure Arithmetic Computation (#66)
[Hao Chen, Ilaria Chillotti, Oxana Poburinnaya, Ilya Razenshteyn and M. Sadegh Riazi] Scaling Up Secure Nearest Neighbor Search (#33)
[Yunhui Long, Vincent Bindschaedler and Carl Gunter] Towards Measuring Membership Privacy (#09) [video]

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