Shuang Song

Affiliations:

Google Research, Brain
University of California, San Diego, USA

According to our database¹, Shuang Song authored at least 30 papers between 2013 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

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In proceedings

Article

PhD thesis

Dataset

Other

Links

On csauthors.net:

Bibliography

2024

Private Learning with Public Features.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Challenges towards the Next Frontier in Privacy.

[BibT_eX]

[DOI]

CoRR, 2023

Multi-Task Differential Privacy Under Distribution Skew.

[BibT_eX]

[DOI]

Abhradeep Guha Thakurta

Li Zhang

Proceedings of the International Conference on Machine Learning, 2023

Measuring Forgetting of Memorized Training Examples.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2022

Toward Training at ImageNet Scale with Differential Privacy.

[BibT_eX]

[DOI]

CoRR, 2022

Membership Inference Attacks From First Principles.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

Differentially Private Model Personalization.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

Evading the Curse of Dimensionality in Unconstrained Private GLMs.

[BibT_eX]

[DOI]

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

Tempered Sigmoid Activations for Deep Learning with Differential Privacy.

[BibT_eX]

[DOI]

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

2020

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

[BibT_eX]

[DOI]

CoRR, 2020

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems.

[BibT_eX]

[DOI]

Shuang Song

Om Thakkar

Abhradeep Thakurta

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Privacy-Preserving Algorithms for Machine Learning.

[BibT_eX]

[DOI]

Shuang Song

PhD thesis, 2018

Differentially Private Continual Release of Graph Statistics.

[BibT_eX]

[DOI]

CoRR, 2018

Scalable Private Learning with PATE.

[BibT_eX]

[DOI]

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

2017

Pufferfish Privacy Mechanisms for Correlated Data.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Shuang Song

Kamalika Chaudhuri

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

2016

Privacy-preserving Analysis of Correlated Data.

[BibT_eX]

[DOI]

Yizhen Wang

Shuang Song

Kamalika Chaudhuri

CoRR, 2016

2015

Learning from Data with Heterogeneous Noise using SGD.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Shuang Song

Kamalika Chaudhuri

Anand D. Sarwate

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

Shuang Song

Timeline

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On csauthors.net:

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