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The objective of machine learning is to extract useful information from data, while privacy is preserved by concealing information.
Application of rough sets in the presumptive diagnosis of urinary system diseases
J.Czerniak and H.Zarzycki · 2002
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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Practical privacy: the SuLQ framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthuramakrishnan Venkitasubramaniam · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Differential privacy for statistics: What we know and what we want to learn
Cynthia Dwork and Adam Smith · 2008
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Composition attacks and auxiliary information in data privacy
Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, and Adam Smith · 2008
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John Pearson, Dietrich Stephan, Stanley Nelson, and David Craig · 2008
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2008
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Efficient, differentially private point estimators
Adam Smith · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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A practical differentially private random decision tree classifier
Geetha Jagannathan, Krishnan Pillaipakkamnatt, and Rebecca N. Wright · 2009
Cited alongside, same era.
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank McSherry · 2009
Cited alongside, same era.
A differentially private graph estimator
Darakhshan J. Mir and Rebecca N. Wright · 2009
Cited alongside, same era.
Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft · 2009
Cited alongside, same era.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
Cited alongside, same era.
Data mining with differential privacy
Arik Friedman and Assaf Schuster · 2010
Cited alongside, same era.
Lower bounds in differential privacy
Anindya De · 2012
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Adaptive differentially private histogram of low-dimensional data
Chengfang Fang and Ee-Chien Chang · 2012
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Beyond worst-case analysis in private singular vector computation
Moritz Hardt and Aaron Roth · 2012
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Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2012
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Differentially-private learning and information theory
Darakhshan J. Mir · 2012
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Differentially private projected histograms: Construction and use for prediction
Staal A. Vinterbo · 2012
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Differentially private data release through multidimensional partitioning
Yonghui Xiao, Li Xiong, and Chun Yuan · 2010
Cited alongside, same era.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Cited alongside, same era.
Publishing set-valued data via differential privacy
Rui Chen, Noman Mohammed, Benjamin C. M. Fung, Bipin C. Desai, and Li Xiong · 2011
Cited alongside, same era.
Personal privacy vs population privacy: learning to attack anonymization
Graham Cormode · 2011
Cited alongside, same era.
Differential privacy
Cynthia Dwork · 2011
Cited alongside, same era.
Differentially private M-estimators
Jing Lei · 2011
Cited alongside, same era.
Functional mechanism: Regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 2012
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Privacy and data-based research
Ori Heffetz and Katrina Ligett · 2013
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Differentially private learning with kernels
Prateek Jain and Abhradeep Thakurta · 2013
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Differential privacy based on importance weighting
Zhanglong Ji and Charles Elkan · 2013
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Differential-private data publishing through component analysis
Xiaoqian Jiang, Zhanglong Ji, Shuang Wang, Noman Mohammed, Samuel Cheng, and Lucila Ohno-Machado · 2013
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On differentially private low rank approximation
Michael Kapralov and Kunal Talwar · 2013
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The composition theorem for differential privacy
Sewoong Oh and Pramod Viswanath · 2013
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Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data
Anand D. Sarwate and Kamalika Chaudhuri · 2013
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Mining frequent graph patterns with differential privacy
Entong Shen and Ting Yu · 2013
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Differentially private naive Bayes classification
Jaideep Vaidya, Basit Shafiq, Anirban Basu, and Yuan Hong · 2013
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Differentially private set-valued data release against incremental updates
Xiaojian Zhang, Xiaofeng Meng, and Rui Chen · 2013
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Differentially private distributed logistic regression using private and public data
Zhanglong Ji, Xiaoqian Jiang, Shuang Wang, Li Xiong, and Lucila Ohno-Machado · 2014
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Differentially private network data release via structural inference
Qian Xiao, Rui Chen, and Kian-Lee Tan · 2014
Closest in time.