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Privacy-preserving machine learning algorithms are crucial for the increasingly common setting in which personal data, such as medical or financial records, are analyzed.
A correspondence between Bayesian estimation on stochastic processes and smoothing by splines
G.S. Kimeldorf and G. Wahba · 1970
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Protocols for secure computations (extended abstract)
Andrew Chi-Chih Yao · 1982
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Probability and measure
P Billingsley · 1995
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An elementary introduction to modern convex geometry
K. Ball · 1997
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Weaving technology and policy together to maintain confidentiality
L. Sweeney · 1997
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Variational Analysis
R.T. Rockafellar and R J-B. Wets · 1998
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Statistical Learning Theory
V. Vapnik · 1998
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The UCI KDD Archive
S. Hettich and S.D. Bay · 1999
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Privacy-preserving data mining
R. Agrawal and R. Srikant · 2000
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k-anonymity: a model for protecting privacy
L. Sweeney · 2002
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Limiting privacy breaches in privacy preserving data mining
A. Evfimievski, J. Gehrke, and R. Srikant · 2003
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Convex Optimization
S. Boyd and L. Vandenberghe · 2004
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The entire regularization path for the support vector machine
T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu · 2004
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When random sampling preserves privacy
K. Chaudhuri and N. Mishra · 2006
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Differential privacy
C. Dwork · 2006
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Cryptographically private support vector machines
S. Laur, H. Lipmaa, and T. Mielikäinen · 2006
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l-diversity: Privacy beyond k-anonymity
A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam · 2006
Cited alongside, same era.
UCI Machine Learning Repository
A. Asuncion and D.J. Newman · 2007
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Wherefore art thou R3579X? Anonymized social networks, hidden patterns, and structural steganography
L. Backstrom, C. Dwork, and J. Kleinberg · 2007
Cited alongside, same era.
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar · 2007
Cited alongside, same era.
Training a support vector machine in the primal
O. Chapelle · 2007
Cited alongside, same era.
”i know what you did last summer”: query logs and user privacy
Rosie Jones, Ravi Kumar, Bo Pang, and Andrew Tomkins · 2007
Cited alongside, same era.
What can we learn privately?
S. A. Kasiviswanathan, H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith · 2008
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Privacy: Theory meets practice on the map
A. Machanavajjhala, D. Kifer, J. M. Abowd, J. Gehrke, and L. Vilhuber · 2008
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Privacy-preserving classification of vertically partitioned data via random kernels
O. L. Mangasarian, E. W. Wild, and G. Fung · 2008
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Robust de-anonymization of large sparse datasets (how to break anonymity of the netflix prize dataset)
A. Narayanan and V. Shmatikov · 2008
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SVM optimization : Inverse dependence on training set size
S. Shalev-Shwartz and N. Srebro · 2008
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Fast rates for regularized objectives
K. Sridharan, N. Srebro, and S. Shalev-Shwartz · 2008
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
Cited alongside, same era.
Smooth sensitivity and sampling in private data analysis
K. Nissim, S. Raskhodnikova, and A. Smith · 2007
Cited alongside, same era.
Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
Cited alongside, same era.
Online Learning: Theory, Algorithms, and Applications
S. Shalev-Shwartz · 2007
Cited alongside, same era.
Privacy-preserving support vector machine classification
J. Z. Zhan and S. Matwin · 2007
Cited alongside, same era.
A learning theory approach to non-interactive database privacy
A. Blum, K. Ligett, and A. Roth · 2008
Cited alongside, same era.
Differential privacy and robust statistics
C. Dwork and J. Lei · 2009
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liblbfgs: a library of limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
N. Okazaki · 2009
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
B. I. P. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft · 2009
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Learning your identity and disease from research papers: information leaks in genome wide association study
Rui Wang, Yong Fuga Li, XiaoFeng Wang, Haixu Tang, and Xiao yong Zhou · 2009
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Differential privacy with compression
S. Zhou, K. Ligett, and L. Wasserman · 2009
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Bounds on the sample complexity for private learning and private data release
Amos Beimel, Shiva Prasad Kasiviswanathan, and Kobbi Nissim · 2010
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Differentially private approximation algorithms
A. Gupta, K. Ligett, F. McSherry, A. Roth, and K. Talwar · 2010
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The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
S.P. Kasivishwanathan, M. Rudelson, A. Smith, and J. Ullman · 2010
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A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
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