Shiva Kasiviswanathan
Shiva Kasiviswanathan
Amazon Machine Learning
Verified email at amazon.com - Homepage
TitleCited byYear
What can we learn privately?
SP Kasiviswanathan, HK Lee, K Nissim, S Raskhodnikova, A Smith
SIAM Journal on Computing 40 (3), 793-826, 2011
4972011
Composition attacks and auxiliary information in data privacy
SR Ganta, SP Kasiviswanathan, A Smith
Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008
3402008
Analyzing graphs with node differential privacy
SP Kasiviswanathan, K Nissim, S Raskhodnikova, A Smith
Theory of Cryptography Conference, 457-476, 2013
1602013
Emerging topic detection using dictionary learning
SP Kasiviswanathan, P Melville, A Banerjee, V Sindhwani
Proceedings of the 20th ACM international conference on Information and …, 2011
1402011
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
SP Kasiviswanathan, M Rudelson, A Smith, J Ullman
Proceedings of the forty-second ACM symposium on Theory of computing, 775-784, 2010
972010
Simple black-box adversarial perturbations for deep networks
N Narodytska, SP Kasiviswanathan
arXiv preprint arXiv:1612.06299, 2016
822016
A note on differential privacy: Defining resistance to arbitrary side information
SP Kasiviswanathan, A Smith
CoRR abs/0803.3946, 2008
762008
Simple black-box adversarial attacks on deep neural networks
N Narodytska, S Kasiviswanathan
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops …, 2017
682017
Algorithms for Counting 2-Sat Solutions and Colorings with Applications
M Fürer, SP Kasiviswanathan
International Conference on Algorithmic Applications in Management, 47-57, 2007
652007
Bounds on the sample complexity for private learning and private data release
A Beimel, SP Kasiviswanathan, K Nissim
Theory of Cryptography Conference, 437-454, 2010
642010
On the'semantics' of differential privacy: A bayesian formulation
SP Kasiviswanathan, A Smith
Journal of Privacy and Confidentiality 6 (1), 2014
632014
Efficient and practical stochastic subgradient descent for nuclear norm regularization
H Avron, S Kale, S Kasiviswanathan, V Sindhwani
arXiv preprint arXiv:1206.6384, 2012
602012
Verifying properties of binarized deep neural networks
N Narodytska, S Kasiviswanathan, L Ryzhyk, M Sagiv, T Walsh
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
572018
Private spatial data aggregation in the local setting
R Chen, H Li, AK Qin, SP Kasiviswanathan, H Jin
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 289-300, 2016
572016
Online l1-dictionary learning with application to novel document detection
SP Kasiviswanathan, H Wang, A Banerjee, P Melville
Advances in Neural Information Processing Systems, 2258-2266, 2012
532012
Online dictionary learning on symmetric positive definite manifolds with vision applications
S Zhang, S Kasiviswanathan, PC Yuen, M Harandi
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
442015
Bounds on the sample complexity for private learning and private data release
A Beimel, H Brenner, SP Kasiviswanathan, K Nissim
Machine learning 94 (3), 401-437, 2014
402014
The power of linear reconstruction attacks
SP Kasiviswanathan, M Rudelson, A Smith
Proceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete …, 2013
332013
Streaming anomaly detection using randomized matrix sketching
H Huang, SP Kasiviswanathan
Proceedings of the VLDB Endowment 9 (3), 192-203, 2015
302015
Subsampled R\'enyi Differential Privacy and Analytical Moments Accountant
YX Wang, B Balle, S Kasiviswanathan
arXiv preprint arXiv:1808.00087, 2018
292018
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