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Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing.
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G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright, “A practical differentially private random decision tree classifier,” in
2009
Cited alongside, same era.
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The weka data mining software: an update,”
2009
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Y. Xiao, L. Xiong, and C. Yuan, “Differentially Private Data Release through Multidimensional Partitioning,” in
2010
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B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: A survey on recent developments,”
2010
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A. Roth and T. Roughgarden, “Interactive privacy via the median mechanism,” in
2010
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M. Hardt and G. Rothblum, “A multiplicative weights mechanism for interactive privacy-preserving data analysis,” in
2010
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A. Inan, M. Kantarcioglu, G. Ghinita, and E. Bertino, “Private record matching using differential privacy,” in
2010
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2011
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D. Kifer and A. Machanavajjhala, “No free lunch in data privacy,” in
2011
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R. Chen, N. Mohammed, B. C. M. Fung, B. C. Desai, and L. Xiong, “Publishing set-valued data via differential privacy,” in
2011
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N. Mohammed, R. Chen, B. C. Fung, and P. S. Yu, “Differentially private data release for data mining,” in
2011
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B. Ding, M. Winslett, J. Han, and Z. Li, “Differentially private data cubes: optimizing noise sources and consistency,” in
2011
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Y. Xiao, J. Gardner, and L. Xiong, “Dpcube: Releasing differentially private data cubes for health information (demo paper),” in
2012
Closest in time.
J. Xu, Z. Zhang, X. Xiao, Y. Yang, and G. Yu, “Differentially private histogram publication,” in
2012
Closest in time.