Shuang Song

According to our database, Shuang Song authored at least 30 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Private Learning with Public Features.

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

CoRR, 2023

Challenges towards the Next Frontier in Privacy.

Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang

CoRR, 2023

Multi-Task Differential Privacy Under Distribution Skew.

Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang

Proceedings of the International Conference on Machine Learning, 2023

Measuring Forgetting of Memorized Training Examples.

Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Debugging Differential Privacy: A Case Study for Privacy Auditing.

Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini

CoRR, 2022

Toward Training at ImageNet Scale with Differential Privacy.

Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta

CoRR, 2022

Membership Inference Attacks From First Principles.

Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr

Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022

EANA: Reducing Privacy Risk on Large-scale Recommendation Models.

Lin Ning, Steve Chien, Shuang Song, Mei Chen, Yunqi Xue, Devora Berlowitz

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Public Data-Assisted Mirror Descent for Private Model Training.

Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta

Proceedings of the International Conference on Machine Learning, 2022

2021

Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning.

Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini

Proceedings of the 42nd IEEE Symposium on Security and Privacy, 2021

Differentially Private Model Personalization.

Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Practical and Private (Deep) Learning Without Sampling or Shuffling.

Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu

Proceedings of the 38th International Conference on Machine Learning, 2021

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates.

Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang

Proceedings of the 38th International Conference on Machine Learning, 2021

Evading the Curse of Dimensionality in Unconstrained Private GLMs.

Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?

Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr

CoRR, 2020

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.

Shuang Song, Om Thakkar, Abhradeep Thakurta

CoRR, 2020

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation.

Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta

CoRR, 2020

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space.

Adam D. Smith, Shuang Song, Abhradeep Thakurta

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

That which we call private.

Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan, Shuang Song

CoRR, 2019

2018

Privacy-Preserving Algorithms for Machine Learning.

Shuang Song

PhD thesis, 2018

Differentially Private Continual Release of Graph Statistics.

Shuang Song, Susan Little, Sanjay Mehta, Staal Amund Vinterbo, Kamalika Chaudhuri

CoRR, 2018

Scalable Private Learning with PATE.

Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Pufferfish Privacy Mechanisms for Correlated Data.

Shuang Song, Yizhen Wang, Kamalika Chaudhuri

Proceedings of the 2017 ACM International Conference on Management of Data, 2017

Renyi Differential Privacy Mechanisms for Posterior Sampling.

Joseph Geumlek, Shuang Song, Kamalika Chaudhuri

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Composition properties of inferential privacy for time-series data.

Shuang Song, Kamalika Chaudhuri

Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016

Privacy-preserving Analysis of Correlated Data.

Yizhen Wang, Shuang Song, Kamalika Chaudhuri

CoRR, 2016

2015

Learning from Data with Heterogeneous Noise using SGD.

Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

The Large Margin Mechanism for Differentially Private Maximization.

Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Stochastic gradient descent with differentially private updates.

Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate

Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013