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Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations.
Proofs that really count: the art of combinatorial proof
Benjamin, A. T. and Quinn, J. J · 2003
Earlier work this paper cites.
Discovering recurring anomalies in text reports regarding complex space systems
Srivastava, A. N. and Zane-Ulman, B · 2005
Earlier work this paper cites.
The challenge problem for automated detection of 101 semantic concepts in multimedia
Snoek, C. G., Worring, M., Van Gemert, J. C., Geusebroek, J. M., and Smeulders, A. W · 2006
Earlier work this paper cites.
Feature hashing for large scale multitask learning
Weinberger, K., Dasgupta, A., Langford, J., Smola, A., and Attenberg, J · 2009
Earlier work this paper cites.
A contextual-bandit approach to personalized news article recommendation
Li, L., Chu, W., Langford, J., and Schapire, R. E · 2010
Earlier work this paper cites.
Web-scale k-means clustering
Sculley, D · 2010
Earlier work this paper cites.
Contextual bandits with linear payoff functions
Chu, W., Li, L., Reyzin, L., and Schapire, R · 2011
Earlier work this paper cites.
Towards privacy for social networks: A zero-knowledge based definition of privacy
Gehrke, J., Lui, E., and Pass, R · 2011
Earlier work this paper cites.
Crowd-blending privacy
Gehrke, J., Hay, M., Lui, E., and Pass, R · 2012
Earlier work this paper cites.
On the use of LSH for privacy preserving personalization
Aghasaryan, A., Bouzid, M., Kostadinov, D., Kothari, M., and Nandi, A · 2013
Cited alongside, same era.
The Algorithmic Foundations of Differential Privacy
Dwork, C. and Roth, A · 2013
Cited alongside, same era.
Rappor: Randomized aggregatable privacy-preserving ordinal response
Erlingsson, Ú., Pihur, V., and Korolova, A · 2014
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Cited alongside, same era.
Recommendations meet web browsing: Enhancing collaborative filtering using internet browsing logs
Achieving privacy in the adversarial multi-armed bandit
Tossou, A. C. Y. and Dimitrakakis, C · 2017
Later among the works it cites.
Corrupt Bandits for Preserving Local Privacy
Gajane, P., Urvoy, T., and Kaufmann, E · 2018
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Machine learning with membership privacy using adversarial regularization
Nasr, M., Shokri, R., and Houmansadr, A · 2018
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Differentially-private” draw and discard” machine learning
Pihur, V., Korolova, A., Liu, F., Sankuratripati, S., Yung, M., Huang, D., and Zeng, R · 2018
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Differentially private contextual linear bandits
Shariff, R. and Sheffet, O · 2018
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Differential privacy for multi-armed bandits: What is it and what is its cost?
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Ronen, R., Yom-Tov, E., and Lavee, G · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Bittau, A., Erlingsson, U., Maniatis, P., Mironov, I., Raghunathan, A., Lie, D., Rudominer, M., Kode, U., Tinnes, J., and Seefeld, B · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Gamified incentives: A badge recommendation model to improve user engagement in social networking websites
Gharibi, R. and Malekzadeh, M · 2017
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al
Cited in the paper.
Basu, D., Dimitrakakis, C., and Tossou, A · 2019
Closest in time.
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., et al · 2019
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Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Erlingsson, Ú., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K., and Thakurta, A · 2020
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Guidelines for implementing and auditing differentially private systems
Kifer, D., Messing, S., Roth, A., Thakurta, A., and Zhang, D · 2020
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