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Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm.
An inverse matrix adjustment arising in discriminant analysis
Bartlett, M. S · 1951
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Randomized response: A survey technique for eliminating evasive answer bias
Warner, S. L · 1965
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How to share a secret
Shamir, A · 1979
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How to play any mental game or a completeness theorem for protocols with honest majority
Goldreich, O., Micali, S., and Wigderson, A · 1987
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 1987
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Completeness theorems for non-cryptographic fault-tolerant distributed computation
Ben-Or, M., Goldwasser, S., and Wigderson, A · 1988
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Efficient multiparty protocols using circuit randomization
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Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems
Kocher, P. C · 1996
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Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation
Damgård, I., Fitzi, M., Kiltz, E., Nielsen, J. B., and Toft, T · 2005
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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The epoch-greedy algorithm for contextual multi-armed bandits
Langford, J. and Zhang, T · 2008
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On the complexity of differentially private data release: Efficient algorithms and hardness results
Dwork, C., Naor, M., Reingold, O., Rothblum, G., and Vadhan, S · 2009
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Foundations of Cryptography
Goldreich, O · 2009
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Improved primitives for secure multiparty integer computation
Catrina, O. and De Hoogh, S · 2010
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Private and continual release of statistics
Chan, T. H., Shi, E., and Song, D · 2010
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Differential privacy under continual observation
Dwork, C., Naor, M., Pitassi, T., and Rothblum, G. N · 2010
Cited alongside, same era.
A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., and Schapire, R. E · 2010
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Multiparty computation from somewhat homomorphic encryption
Damgård, I., Pastro, V., Smart, N., and Zakarias, S · 2011
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Differential privacy
Dwork, C · 2011
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J · 2016
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Algorithms for differentially private multi-armed bandits
Tossou, A. and Dimitrakakis, C · 2016
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Practical secure aggregation for privacy preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
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SecureML: A system for scalable privacy-preserving machine learning
Mohassel, P. and Zhang, Y · 2017
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Chameleon: A hybrid secure computation framework for machine learning applications
Riazi, M. S., Weinert, C., Tkachenko, O., Songhori, E. M., Schneider, T., and Koushanfar, F · 2017
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Efficient deep learning on multi-source private data
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(leveled) fully homomorphic encryption without bootstrapping
Brakerski, Z., Gentry, C., and Vaikuntanathan, V · 2012
Cited alongside, same era.
Differentially private online learning
Jain, P., Kothari, P., and Thakurta, A · 2012
Cited alongside, same era.
(nearly) Optimal algorithms for private online learning in full-information and bandit settings
Thakurta, A. G. and Smith, A. D · 2013
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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ABY – a framework for efficient mixed-protocol secure two-party computation
Demmler, D., Schneider, T., and Zohner, M · 2015
Cited alongside, same era.
(nearly) optimal differentially private stochastic multi-arm bandits
Mishra, N. and Thakurta, A · 2015
Cited alongside, same era.
Hynes, N., Cheng, R., and Song, D · 2018
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Gazelle: A low latency framework for secure neural network inference
Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A · 2018
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Differentially private contextual linear bandits
Shariff, R. and Sheffet, O · 2018
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SecureNN: Efficient and private neural network training
Wagh, S., Gupta, D., and Chandran, N · 2018
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Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., Overveldt, T. V., Petrou, D., Ramage, D., and Roselander, J · 2019
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Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation
Cock, M. D., Dowsley, R., Horst, C., Katti, R., Nascimento, A. C. A., Poon, W.-S., and Truex, S · 2019
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Privacy-preserving classification of personal text messages with secure multi-party computation: An application to hate-speech detection
Reich, D., Todoki, A., Dowsley, R., Cock, M. D., and Nascimento, A · 2019
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