Together or Alone: The Price of Privacy in Collaborative Learning

[1] Michela Chessa, Jens Grossklags, and Patrick Loiseau. A game-theoretic study on non-monetary incentives in data analytics projects with privacy implications. In Computer Security Foundations Symposium (CSF), 2015 IEEE 28th. IEEE, 2015.10.1109/CSF.2015.14Search in Google Scholar

[2] Cynthia Dwork. Differential privacy. In Proceedings of the 33rd international conference on Automata, Languages and Programming. ACM, 2006.10.1007/11787006_1Search in Google Scholar

[3] Arik Friedman, Shlomo Berkovsky, and Mohamed Ali Kaafar. A differential privacy framework for matrix factorization recommender systems. User Modeling and User-Adapted Interaction, 2016.10.1007/s11257-016-9177-7Search in Google Scholar

[4] Jihun Hamm, Yingjun Cao, and Mikhail Belkin. Learning privately from multiparty data. In International Conference on Machine Learning, 2016.Search in Google Scholar

[5] J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2012.Search in Google Scholar

[6] John C Harsanyi, Reinhard Selten, et al. A general theory of equilibrium selection in games. MIT Press Books, 1988.Search in Google Scholar

[7] Stratis Ioannidis and Patrick Loiseau. Linear regression as a non-cooperative game. In International Conference on Web and Internet Economics. Springer, 2013.10.1007/978-3-642-45046-4_23Search in Google Scholar

[8] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009.10.1109/MC.2009.263Search in Google Scholar

[9] Elias Koutsoupias and Christos Papadimitriou. Worst-case equilibria. In Stacs. Springer, 1999.10.1007/3-540-49116-3_38Search in Google Scholar

[10] Fabio Martinelli, Andrea Saracino, and Mina Sheikhalishahi. Modeling privacy aware information sharing systems: A formal and general approach. In Trustcom/BigDataSE/ISPA, 2016 IEEE, pages 767–774. IEEE, 2016.10.1109/TrustCom.2016.0137Search in Google Scholar

[11] H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629, 2016.Search in Google Scholar

[12] Dov Monderer and Lloyd S Shapley. Potential games. Games and economic behavior, 1996.10.1006/game.1996.0044Search in Google Scholar

[13] Manas Pathak, Shantanu Rane, and Bhiksha Raj. Multiparty differential privacy via aggregation of locally trained classifiers. In Advances in Neural Information Processing Systems, 2010.Search in Google Scholar

[14] Jeffrey Pawlick and Quanyan Zhu. A stackelberg game perspective on the conflict between machine learning and data obfuscation. In Information Forensics and Security (WIFS), 2016 IEEE International Workshop on, 2016.10.1109/WIFS.2016.7823893Search in Google Scholar

[15] Balazs Pejo. Matrix factorisation in matlab via stochastic gradient descent. https://github.com/pidzso/ML.Search in Google Scholar

[16] Arun Rajkumar and Shivani Agarwal. A differentially private stochastic gradient descent algorithm for multiparty classification. In Artificial Intelligence and Statistics, 2012.Search in Google Scholar

[17] Mina Sheikhalishahi and Fabio Martinelli. Privacy-utility feature selection as a privacy mechanism in collaborative data classification. In Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2017 IEEE 26th International Conference on, pages 244–249. IEEE, 2017.10.1109/WETICE.2017.15Search in Google Scholar

[18] Xiaotong Wu, Taotao Wu, Maqbool Khan, Qiang Ni, and Wanchun Dou. Game theory based correlated privacy preserving analysis in big data. IEEE Transactions on Big Data, 2017.Search in Google Scholar

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