Shuang Song

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 39

Top Venues

CoRR

ICML

AISTATS

IEEE Symposium on Security and Privacy

University of California, San Diego, USA (PHD)

Education History

University of California, San Diego, USA

PHD

Publications

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Learning with Public Features. AISTATS (2024)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. CoRR (2023)
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Learning with Public Features. CoRR (2023)
Walid Krichene, Prateek Jain, Shuang Song, Mukund Sundararajan, Abhradeep Guha Thakurta, Li Zhang
Multi-Task Differential Privacy Under Distribution Skew. ICML (2023)
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
Challenges towards the Next Frontier in Privacy. CoRR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Guha Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. ICLR (2023)
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito, Katherine Lee, Nicholas Carlini, Eric Wallace, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Chiyuan Zhang
Measuring Forgetting of Memorized Training Examples. CoRR (2022)
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
Membership Inference Attacks From First Principles. IEEE Symposium on Security and Privacy (2022)
Lin Ning, Steve Chien, Shuang Song, Mei Chen, Yunqi Xue, Devora Berlowitz
EANA: Reducing Privacy Risk on Large-scale Recommendation Models. RecSys (2022)
Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta
Toward Training at ImageNet Scale with Differential Privacy. CoRR (2022)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. ICML (2022)
Florian Tramèr, Andreas Terzis, Thomas Steinke, Shuang Song, Matthew Jagielski, Nicholas Carlini
Debugging Differential Privacy: A Case Study for Privacy Auditing. CoRR (2022)
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. ICML (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning without Sampling or Shuffling. CoRR (2021)
Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta
Evading the Curse of Dimensionality in Unconstrained Private GLMs. AISTATS (2021)
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. IEEE Symposium on Security and Privacy (2021)
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramèr
Membership Inference Attacks From First Principles. CoRR (2021)
Prateek Jain, John Rush, Adam D. Smith, Shuang Song, Abhradeep Guha Thakurta
Differentially Private Model Personalization. NeurIPS (2021)
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu
Practical and Private (Deep) Learning Without Sampling or Shuffling. ICML (2021)
Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. CoRR (2021)
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, Nicholas Carlini
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. CoRR (2021)
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. AAAI (2021)
Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta
Public Data-Assisted Mirror Descent for Private Model Training. CoRR (2021)
Adam D. Smith, Shuang Song, Abhradeep Thakurta
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. NeurIPS (2020)
Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, Úlfar Erlingsson
Tempered Sigmoid Activations for Deep Learning with Differential Privacy. CoRR (2020)
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramèr
An Attack on InstaHide: Is Private Learning Possible with Instance Encoding? CoRR (2020)
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, Abhradeep Thakurta
Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. CoRR (2020)
Shuang Song, Om Thakkar, Abhradeep Thakurta
Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. CoRR (2020)
Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan, Shuang Song
That which we call private. CoRR (2019)
Shuang Song, Susan Little, Sanjay Mehta, Staal A. Vinterbo, Kamalika Chaudhuri
Differentially Private Continual Release of Graph Statistics. CoRR (2018)
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
Scalable Private Learning with PATE. CoRR (2018)
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, Úlfar Erlingsson
Scalable Private Learning with PATE. ICLR (2018)
Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
Renyi Differential Privacy Mechanisms for Posterior Sampling. NIPS (2017)
Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
Rényi Differential Privacy Mechanisms for Posterior Sampling. CoRR (2017)
Shuang Song, Yizhen Wang, Kamalika Chaudhuri
Pufferfish Privacy Mechanisms for Correlated Data. SIGMOD Conference (2017)
Shuang Song, Kamalika Chaudhuri
Composition Properties of Inferential Privacy for Time-Series Data. CoRR (2017)
Shuang Song, Kamalika Chaudhuri
Composition properties of inferential privacy for time-series data. Allerton (2017)
Yizhen Wang, Shuang Song, Kamalika Chaudhuri
Privacy-preserving Analysis of Correlated Data. CoRR (2016)
Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
Learning from Data with Heterogeneous Noise using SGD. AISTATS (2015)
Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
The Large Margin Mechanism for Differentially Private Maximization. NIPS (2014)
Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
Learning from Data with Heterogeneous Noise using SGD. CoRR (2014)
Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
The Large Margin Mechanism for Differentially Private Maximization. CoRR (2014)
Shuang Song, Kamalika Chaudhuri, Anand D. Sarwate
Stochastic gradient descent with differentially private updates. GlobalSIP (2013)

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Publication Activity (10 Years)

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Shuang Song