Email: shs037 at eng dot ucsd dot edu
I got my PhD from UC San Diego in Machine Learning and Differential Privacy under the supervision of Prof. Kamalika Chaudhuri. Before joining UCSD, I obtained my BSc degree in Mathematics and Computer Science from The Hong Kong University of Science and Technology.
I work at Google Brain.
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EANA: Reducing Privacy Risk on Large-scale Recommendation Models Devora Berlowitz, Lin Ning, Mei Chen, QiQi Xue, Shuang Song, Steve Chien
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Public data-assisted mirror descent for private model training [pdf] Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta ICML 2022
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Membership inference attacks from first principles [pdf] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, Florian Tramer S&P 2022
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Toward training at imagenet scale with differential privacy [pdf] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, Abhradeep Thakurta
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Differentially Private Model Personalization [pdf] Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta NeurIPS 2021
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Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates [pdf] Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang ICML 2021
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Practical and private (deep) learning without sampling or shuffling [pdf] [code] Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu ICML 2021
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Evading the curse of dimensionality in unconstrained private GLMs [pdf] Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta AISTATS, 2021
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Tempered sigmoid activations for deep learning with differential privacy [pdf] Nicolas Papernot, Abhradeep Thakurta, Shuang Song, Steve Chien, and Úlfar Erlingsson AAAI Conference on Artificial Intelligence, 2021
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The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space [pdf] [code] Adam Smith, Shuang Song, and Abhradeep Thakurta Neural Information Processing Systems (NeurIPS), 2020
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Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation [pdf] Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta
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Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels [pdf] [code] Shuang Song, David Berthelot, and Afshin Rostamizadeh
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That which we call private [pdf] Úlfar Erlingsson, Ilya Mironov, Ananth Raghunathan, and Shuang Song
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Scalable Private Learning with PATE [pdf] Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson International Conference on Learning Representations (ICLR), 2018
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Rényi Differential Privacy Mechanisms for Posterior Sampling [pdf] Joseph Geumlek, Shuang Song, and Kamalika Chaudhuri Neural Information Processing Systems (NIPS), 2017
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Composition Properties of Inferential Privacy for Time-Series Data [pdf] Shuang Song, and Kamalika Chaudhuri Allerton Conference on Communication, Control and Computing, 2017
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Pufferfish Privacy Mechanisms for Correlated Data [pdf] Shuang Song, Yizhen Wang, and Kamalika Chaudhuri ACM SIGMOD International Conference on Management of Data (SIGMOD), 2017
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Learning from Data with Heterogenous Noise using SGD [pdf] Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate International Conference on Artificial Intelligence and Statistics (AISTATS) 2015
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The Large Margin Mechanism for Differentially Private Maximization [pdf] Kamalika Chaudhuri, Daniel Hsu, and Shuang Song Neural Information Processing Systems (NIPS) 2014
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Stochastic Gradient Descent with Differentially Private Updates [pdf] Shuang Song, Kamalika Chaudhuri, and Anand Sarwate GlobalSIP Conference, 2013
- Teaching Assistant of CSE 151: Introduction to AI: A Statistical Approach, Winter 2017, Winter 2016, Spring 2014
- Teaching Assistant of CSE 250C: Machine Learning Theory, Spring 2016