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Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data.
I am not what i write: Privacy preserving text representation learning
Ghazaleh Beigi, Kai Shu, Ruocheng Guo, Suhang Wang, and Huan Liu. 2019 · 1907
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On the generalised distance in statistics
Prasanta Chandra Mahalanobis. 1936 · 1936
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Weaving technology and policy together to maintain confidentiality
Latanya Sweeney. 1997 · 1997
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The mahalanobis distance
Roy De Maesschalck, Delphine Jouan-Rimbaud, and Désiré L Massart. 2000 · 2000
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Shrinkage estimators for covariance matrices
Michael J Daniels and Robert E Kass. 2001 · 2001
Earlier work this paper cites.
A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics
Juliane Schäfer and Korbinian Strimmer. 2005 · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
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Differential privacy: A survey of results
Cynthia Dwork. 2008 · 2008
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
et al. Homer N, Szelinger S. 2008 · 2008
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
A. Narayanan and V. Shmatikov. 2008 · 2008
Earlier work this paper cites.
Learning a mahalanobis distance metric for data clustering and classification
Shiming Xiang, Feiping Nie, and Changshui Zhang. 2008 · 2008
Earlier work this paper cites.
h (k)-private information retrieval from privacy-uncooperative queryable databases
Josep Domingo-Ferrer, Agusti Solanas, and Jordi Castellà-Roca. 2009 · 2009
Earlier work this paper cites.
Genomic privacy and limits of individual detection in a pool
et al. Sankararaman S., Obozinski G. 2009 · 2009
Earlier work this paper cites.
Embellishing text search queries to protect user privacy
Hwee Hwa Pang, Xuhua Ding, and Xiaokui Xiao. 2010 · 2010
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Contributions to the study of sms spam filtering: new collection and results
Tiago A Almeida, José María G Hidalgo, and Akebo Yamakami. 2011 · 2011
Earlier work this paper cites.
A machine learning based system for semi-automatically redacting documents
Chad Cumby and Rayid Ghani. 2011 · 2011
Cited alongside, same era.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith. 2011 · 2011
Cited alongside, same era.
Use of mahalanobis distance for detecting outliers and outlier clusters in markedly non-normal data: a vehicular traffic example
Rik Warren, Robert F Smith, and Anne K Cybenko. 2011 · 2011
Cited alongside, same era.
t-plausibility: Generalizing words to desensitize text
Balamurugan Anandan, Chris Clifton, Wei Jiang, Mummoorthy Murugesan, Pedro Pastrana-Camacho, and Luo Si. 2012 · 2012
Cited alongside, same era.
New Statistical Applications for Differential Privacy
Rob Hall et al. 2012 · 2012
Cited alongside, same era.
Geo-indistinguishability: Differential privacy for location-based systems
Differentially private analysis of outliers
Rina Okada, Kazuto Fukuchi, and Jun Sakuma. 2015 · 2015
Later among the works it cites.
Peas: Private, efficient and accurate web search
Albin Petit, Thomas Cerqueus, Sonia Ben Mokhtar, Lionel Brunie, and Harald Kosch. 2015 · 2015
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A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection
Yuxiang Zhang, Bo Du, Liangpei Zhang, and Shugen Wang. 2015 · 2015
Later among the works it cites.
Mahalanobis distance based on fuzzy clustering algorithm for image segmentation
Xuemei Zhao, Yu Li, and Quanhua Zhao. 2015 · 2015
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On the (in) effectiveness of mosaicing and blurring as tools for document redaction
Steven Hill, Zhimin Zhou, Lawrence Saul, and Hovav Shacham. 2016 · 2016
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Miguel E Andrés, Nicolás E Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013 · 2013
Cited alongside, same era.
Broadening the scope of differential privacy using metrics
Konstantinos Chatzikokolakis, Miguel E Andrés, Nicolás Emilio Bordenabe, and Catuscia Palamidessi. 2013 · 2013
Cited alongside, same era.
Differential privacy for functions and functional data
Rob Hall, Alessandro Rinaldo, and Larry Wasserman. 2013 · 2013
Cited alongside, same era.
Knowledge-based scheme to create privacy-preserving but semantically-related queries for web search engines
David SáNchez, Jordi Castellà-Roca, and Alexandre Viejo. 2013 · 2013
Cited alongside, same era.
Large dimensional analysis and optimization of robust shrinkage covariance matrix estimators
Romain Couillet and Matthew McKay. 2014 · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Cited alongside, same era.
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016 · 2016
Later among the works it cites.
C-sanitized: A privacy model for document redaction and sanitization
David Sánchez and Montserrat Batet. 2016 · 2016
Later among the works it cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Later among the works it cites.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov. 2017 · 2017
Later among the works it cites.
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2018 · 2018
Later among the works it cites.
Technical privacy metrics: a systematic survey
Isabel Wagner and David Eckhoff. 2018 · 2018
Later among the works it cites.
Generalised differential privacy for text document processing
Natasha Fernandes, Mark Dras, and Annabelle McIver. 2019 · 2019
Later among the works it cites.
Leveraging hierarchical representations for preserving privacy and utility in text
Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. 2019 · 2019
Later among the works it cites.
Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations
Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, and Tom Diethe. 2020 · 2020
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