Policy impacts of statistical uncertainty and privacy von Steed, Ryan, Liu, Terrance, Wu, Zhiwei Steven, Acquisti, Alessandro

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Confidence-ranked reconstruction of census microdata from published statistics von Dick, Travis, Dwork, Cynthia, Kearns, Michael, Liu, Terrance, Roth, Aaron, Vietri, Giuseppe, Wu, Zhiwei Steven

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Response to comment on "Policy impacts of statistical uncertainty and privacy" von Steed, Ryan, Acquisti, Alessandro, Wu, Zhiwei Steven, Liu, Terrance

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Reply to Sanchéz et al.: Multiplicity does not protect privacy von Dick, Travis, Dwork, Cynthia, Kearns, Michael, Liu, Terrance, Roth, Aaron, Vietri, Giuseppe, Wu, Zhiwei Steven

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Confidence-ranked reconstruction of census records from aggregate statistics fails to capture privacy risks and reidentifiability/Reply to Sanchéz et al.: Multiplicity does not pro... von Sánchez, David, Domingo-Ferrer, Josep, Muralidhar, Krishnamurty, Dick, Travis, Dwork, Cynthia, Kearns, Michael, Liu, Terrance, Roth, Aaron, Vietri, Giuseppe, Wu, Zhiwei Steven

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Multi-group Uncertainty Quantification for Long-form Text Generation von Liu, Terrance, Wu, Zhiwei Steven

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Private Synthetic Data with Hierarchical Structure von Liu, Terrance, Wu, Zhiwei Steven

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Multi-group Uncertainty Quantification for Long-form Text Generation von Liu, Terrance, Wu, Zhiwei Steven

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Generating Private Synthetic Data with Genetic Algorithms von Liu, Terrance, Tang, Jingwu, Vietri, Giuseppe, Wu, Zhiwei Steven

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Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods von Liu, Terrance, Vietri, Giuseppe, Wu, Zhiwei Steven

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Private Synthetic Data with Hierarchical Structure von Liu, Terrance, Wu, Zhiwei Steven

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Confidence-Ranked Reconstruction of Census Microdata from Published Statistics von Dick, Travis, Dwork, Cynthia, Kearns, Michael, Liu, Terrance, Roth, Aaron, Vietri, Giuseppe, Wu, Zhiwei Steven

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Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods von Liu, Terrance, Vietri, Giuseppe, Wu, Zhiwei Steven

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Generating Private Synthetic Data with Genetic Algorithms von Liu, Terrance, Tang, Jingwu, Vietri, Giuseppe, Wu, Zhiwei Steven

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Leveraging Public Data for Practical Private Query Release von Liu, Terrance, Vietri, Giuseppe, Steinke, Thomas, Ullman, Jonathan, Wu, Zhiwei Steven

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Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study von Liu, Terrance, Liang, Paul Pu, Muszynski, Michal, Ishii, Ryo, Brent, David, Auerbach, Randy, Allen, Nicholas, Morency, Louis-Philippe

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Think Locally, Act Globally: Federated Learning with Local and Global Representations von Liang, Paul Pu, Liu, Terrance, Ziyin, Liu, Allen, Nicholas B, Auerbach, Randy P, Brent, David, Salakhutdinov, Ruslan, Morency, Louis-Philippe

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Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data von Liang, Paul Pu, Liu, Terrance, Cai, Anna, Muszynski, Michal, Ishii, Ryo, Allen, Nicholas, Auerbach, Randy, Brent, David, Salakhutdinov, Ruslan, Morency, Louis-Philippe

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Leveraging Public Data for Practical Private Query Release von Liu, Terrance, Vietri, Giuseppe, Steinke, Thomas, Ullman, Jonathan, Wu, Zhiwei Steven

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Think Locally, Act Globally: Federated Learning with Local and Global Representations von Paul Pu Liang, Liu, Terrance, Liu Ziyin, Allen, Nicholas B, Auerbach, Randy P, Brent, David, Salakhutdinov, Ruslan, Louis-Philippe Morency

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