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The protection of private information is of vital importance in data-driven research, business, and government.
Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Prabhakar Raghavan and Clark D Tompson · 1987
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On the complexity of optimal microaggregation for statistical disclosure control
Anna Oganian and Josep Domingo-Ferrer · 2001
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Achieving k-anonymity privacy protection using generalization and suppression
Latanya Sweeney · 2002
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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On the complexity of optimal k-anonymity
Adam Meyerson and Ryan Williams · 2004
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Ordinal, continuous and heterogeneous k-anonymity through microaggregation
Josep Domingo-Ferrer and Vicenç Torra · 2005
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A norm compression inequality for block partitioned positive semidefinite matrices
Koenraad MR Audenaert · 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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M. Hardt and G. N. Rothblum, “A multiplicative weights mechanism for privacy-preserving data analysis,” in 2010 IEEE 51st Annual Symposium on Foundations of Computer Science . IEEE, 2010, pp. 61–70
2010
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Provably private data anonymization: Or, k-anonymity meets differential privacy
Ninghui Li, Wahbeh H Qardaji, and Dong Su · 2011
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PCPs and the hardness of generating private synthetic data
Jonathan Ullman and Salil Vadhan · 2011
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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Faster algorithms for privately releasing marginals
Justin Thaler, Jonathan Ullman, and Salil Vadhan · 2012
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A. Blum, K. Ligett, and A. Roth, “A learning theory approach to noninteractive database privacy,” Journal of the ACM (JACM) , vol. 60, no. 2, pp. 1–25, 2013
2013
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On differentially private low rank approximation
Michael Kapralov and Kunal Talwar · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Differentially private synthesization of multi-dimensional data using copula functions
Haoran Li, Li Xiong, and Xiaoqian Jiang · 2014
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Enhancing data utility in differential privacy via microaggregation-based k-anonymity
Jordi Soria-Comas, Josep Domingo-Ferrer, David Sánchez, and Sergio Martínez · 2014
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Efficient algorithms for privately releasing marginals via convex relaxations
Privacy and synthetic datasets
Steven M Bellovin, Preetam K Dutta, and Nathan Reitinger · 2019
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E-voting scheme using secret sharing and k-anonymity
Yining Liu and Quanyu Zhao · 2019
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Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
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Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis
David Rebollo Monedero, Ahmad Mohamad Mezher, Xavier Casanova Colomé, Jordi Forné, and Miguel Soriano · 2019
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The Age of Surveillance Capitalism: The Fight for the Future at the New Frontier of Power
Shoshana Zuboff · 2019
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Mathematics of Data Science
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Cynthia Dwork, Aleksandar Nikolov, and Kunal Talwar · 2015
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Iterated local search for microaggregation
Michael Laszlo and Sumitra Mukherjee · 2015
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Database anonymization: privacy models, data utility, and microaggregation-based inter-model connections
Josep Domingo-Ferrer, David Sánchez, and Jordi Soria-Comas · 2016
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Utility-preserving differentially private data releases via individual ranking microaggregation
David Sánchez, Josep Domingo-Ferrer, Sergio Martínez, and Jordi Soria-Comas · 2016
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A k-anonymity based schema for location privacy preservation
Fan Fei, Shu Li, Haipeng Dai, Chunhua Hu, Wanchun Dou, and Qiang Ni · 2017
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Datasynthesizer: Privacy-preserving synthetic datasets
Haoyue Ping, Julia Stoyanovich, and Bill Howe · 2017
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Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Afonso Bandeira, Amit Singer, and Thomas Strohmer · 2020
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Probability and random processes
Geoffrey Grimmett and David Stirzaker · 2020
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How much still needs to be done to make algorithms more ethical
Michael Kearns and Aaron Roth · 2020
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θ \theta -sensitive k-anonymity: An anonymization model for IoT based electronic health records
Razaullah Khan, Xiaofeng Tao, Adeel Anjum, Tehsin Kanwal, Abid Khan, Carsten Maple, et al · 2020
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An Introduction to the Geometry of N Dimensions
Duncan McLaren-Young-Sommerville · 2020
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Hardness of k-anonymous microaggregation
Florian Thaeter and Rüdiger Reischuk · 2020
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Leveraging public data for practical private query release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Zhiwei Steven Wu · 2021
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