Stochastic gradient descent with differentially private updates. - Shuang Song 001; Kamalika Chaudhuri; Anand D. Sarwate (GlobalSIP, 2013)

The Large Margin Mechanism for Differentially Private Maximization. - Kamalika Chaudhuri; Daniel J. Hsu; Shuang Song 001 (NIPS, 2014)

Learning from Data with Heterogeneous Noise using SGD. - Shuang Song 001; Kamalika Chaudhuri; Anand D. Sarwate (AISTATS, 2015)

Privacy-preserving Analysis of Correlated Data. - Yizhen Wang; Shuang Song 001; Kamalika Chaudhuri (CoRR, 2016)

Composition properties of inferential privacy for time-series data. - Shuang Song 001; Kamalika Chaudhuri (Allerton, 2017)

Renyi Differential Privacy Mechanisms for Posterior Sampling. - Joseph Geumlek; Shuang Song 001; Kamalika Chaudhuri (NIPS, 2017)

Pufferfish Privacy Mechanisms for Correlated Data. - Shuang Song 001; Yizhen Wang; Kamalika Chaudhuri (SIGMOD Conference, 2017)

Scalable Private Learning with PATE. - Nicolas Papernot; Shuang Song 001; Ilya Mironov; Ananth Raghunathan; Kunal Talwar; Úlfar Erlingsson (ICLR, 2018)

Differentially Private Continual Release of Graph Statistics. - Shuang Song 001; Susan Little; Sanjay Mehta; Staal Amund Vinterbo; Kamalika Chaudhuri (CoRR, 2018)

Privacy-Preserving Algorithms for Machine Learning. - Shuang Song 001 (2018)

That which we call private. - Úlfar Erlingsson; Ilya Mironov; Ananth Raghunathan; Shuang Song 001 (CoRR, 2019)

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space. - Adam D. Smith 001; Shuang Song 001; Abhradeep Thakurta (NeurIPS, 2020)

Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation. - Úlfar Erlingsson; Vitaly Feldman; Ilya Mironov; Ananth Raghunathan; Shuang Song 001; Kunal Talwar; Abhradeep Thakurta (CoRR, 2020)

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems. - Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta (CoRR, 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 001; Abhradeep Thakurta; Florian Tramèr (CoRR, 2020)

Tempered Sigmoid Activations for Deep Learning with Differential Privacy. - Nicolas Papernot; Abhradeep Thakurta; Shuang Song 001; Steve Chien; Úlfar Erlingsson (AAAI, 2021)

Evading the Curse of Dimensionality in Unconstrained Private GLMs. - Shuang Song 001; Thomas Steinke 002; Om Thakkar 001; Abhradeep Thakurta (AISTATS, 2021)

Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. - Steve Chien; Prateek Jain 002; Walid Krichene; Steffen Rendle; Shuang Song 001; Abhradeep Thakurta; Li Zhang 001 (ICML, 2021)

Practical and Private (Deep) Learning Without Sampling or Shuffling. - Peter Kairouz; Brendan McMahan; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Zheng Xu 002 (ICML, 2021)

Differentially Private Model Personalization. - Prateek Jain 002; John Rush; Adam D. Smith 001; Shuang Song 001; Abhradeep Guha Thakurta (NeurIPS, 2021)

Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. - Milad Nasr; Shuang Song 001; Abhradeep Thakurta; Nicolas Papernot; Nicholas Carlini (SP, 2021)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

EANA: Reducing Privacy Risk on Large-scale Recommendation Models. - Lin Ning 001; Steve Chien; Shuang Song 001; Mei Chen; Yunqi Xue; Devora Berlowitz (RecSys, 2022)

Membership Inference Attacks From First Principles. - Nicholas Carlini; Steve Chien; Milad Nasr; Shuang Song 001; Andreas Terzis; Florian Tramèr (SP, 2022)

Toward Training at ImageNet Scale with Differential Privacy. - Alexey Kurakin; Steve Chien; Shuang Song 001; Roxana Geambasu; Andreas Terzis; Abhradeep Thakurta (CoRR, 2022)

Debugging Differential Privacy: A Case Study for Privacy Auditing. - Florian Tramèr; Andreas Terzis; Thomas Steinke 002; Shuang Song 001; Matthew Jagielski; Nicholas Carlini (CoRR, 2022)

Measuring Forgetting of Memorized Training Examples. - Matthew Jagielski; Om Thakkar 001; Florian Tramèr; Daphne Ippolito; Katherine Lee; Nicholas Carlini; Eric Wallace; Shuang Song 001; Abhradeep Guha Thakurta; Nicolas Papernot; Chiyuan Zhang (ICLR, 2023)

Multi-Task Differential Privacy Under Distribution Skew. - Walid Krichene; Prateek Jain 002; Shuang Song 001; Mukund Sundararajan; Abhradeep Guha Thakurta; Li Zhang 001 (ICML, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

Private Learning with Public Features. - Walid Krichene; Nicolas Mayoraz; Steffen Rendle; Shuang Song 001; Abhradeep Thakurta; Li Zhang 001 (AISTATS, 2024)

Recycling Scraps: Improving Private Learning by Leveraging Checkpoints]{Recycling Scraps: Improving Private Learning by Leveraging Checkpoints. - Virat Shejwalkar; Arun Ganesh; Rajiv Mathews; Yarong Mu; Shuang Song 001; Om Thakkar 001; Abhradeep Thakurta; Xinyi Zheng (Proc. Priv. Enhancing Technol., 2025)

Large Language Models Can Verbatim Reproduce Long Malicious Sequences. - Sharon Lin; Krishnamurthy Dvijotham; Jamie Hayes; Chongyang Shi 003; Ilia Shumailov; Shuang Song 001 (CoRR, 2025)

Lessons from Defending Gemini Against Indirect Prompt Injections. - Chongyang Shi 003; Sharon Lin; Shuang Song 001; Jamie Hayes; Ilia Shumailov; Itay Yona; Juliette Pluto; Aneesh Pappu; Christopher A. Choquette-Choo; Milad Nasr; Chawin Sitawarin; Gena Gibson; et al. (CoRR, 2025)