Shuang Song
Shuang Song
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Stochastic gradient descent with differentially private updates
S Song, K Chaudhuri, AD Sarwate
2013 IEEE global conference on signal and information processing, 245-248, 2013
Scalable private learning with pate
N Papernot, S Song, I Mironov, A Raghunathan, K Talwar, ┌ Erlingsson
arXiv preprint arXiv:1802.08908, 2018
Membership inference attacks from first principles
N Carlini, S Chien, M Nasr, S Song, A Terzis, F Tramer
2022 IEEE Symposium on Security and Privacy (SP), 1897-1914, 2022
Pufferfish privacy mechanisms for correlated data
S Song, Y Wang, K Chaudhuri
Proceedings of the 2017 ACM International Conference on Management of Dataá…, 2017
Tempered sigmoid activations for deep learning with differential privacy
N Papernot, A Thakurta, S Song, S Chien, ┌ Erlingsson
Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 9312-9321, 2021
Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
┌ Erlingsson, V Feldman, I Mironov, A Raghunathan, S Song, K Talwar, ...
arXiv preprint arXiv:2001.03618, 2020
Practical and private (deep) learning without sampling or shuffling
P Kairouz, B McMahan, S Song, O Thakkar, A Thakurta, Z Xu
International Conference on Machine Learning, 5213-5225, 2021
Evading the curse of dimensionality in unconstrained private glms
S Song, T Steinke, O Thakkar, A Thakurta
International Conference on Artificial Intelligence and Statistics, 2638-2646, 2021
Renyi differential privacy mechanisms for posterior sampling
J Geumlek, S Song, K Chaudhuri
Advances in Neural Information Processing Systems 30, 2017
Toward training at imagenet scale with differential privacy
A Kurakin, S Song, S Chien, R Geambasu, A Terzis, A Thakurta
arXiv preprint arXiv:2201.12328, 2022
The large margin mechanism for differentially private maximization
K Chaudhuri, DJ Hsu, S Song
Advances in Neural Information Processing Systems 27, 2014
Learning from data with heterogeneous noise using sgd
S Song, K Chaudhuri, A Sarwate
Artificial Intelligence and Statistics, 894-902, 2015
Public data-assisted mirror descent for private model training
E Amid, A Ganesh, R Mathews, S Ramaswamy, S Song, T Steinke, ...
International Conference on Machine Learning, 517-535, 2022
Combining mixmatch and active learning for better accuracy with fewer labels
S Song, D Berthelot, A Rostamizadeh
arXiv preprint arXiv:1912.00594, 2019
The flajolet-martin sketch itself preserves differential privacy: Private counting with minimal space
A Smith, S Song, A Guha Thakurta
Advances in Neural Information Processing Systems 33, 19561-19572, 2020
Differentially private continual release of graph statistics
S Song, S Little, S Mehta, S Vinterbo, K Chaudhuri
arXiv preprint arXiv:1809.02575, 2018
Differentially private model personalization
P Jain, J Rush, A Smith, S Song, A Guha Thakurta
Advances in Neural Information Processing Systems 34, 29723-29735, 2021
That which we call private
┌ Erlingsson, I Mironov, A Raghunathan, S Song
arXiv preprint arXiv:1908.03566, 2019
Private alternating least squares: Practical private matrix completion with tighter rates
S Chien, P Jain, W Krichene, S Rendle, S Song, A Thakurta, L Zhang
International Conference on Machine Learning, 1877-1887, 2021
Composition properties of inferential privacy for time-series data
S Song, K Chaudhuri
2017 55th Annual Allerton Conference on Communication, Control, andá…, 2017
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