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We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained.
Y. Lindell and B. Pinkas, “Privacy preserving data mining,” in CRYPTO , 2000
2000
Earlier work this paper cites.
J. Zhu and T. Hastie, “Kernel logistic regression and the import vector machine,” in NIPS , 2001
2001
Earlier work this paper cites.
W. Du, Y. Han, and S. Chen, “Privacy-preserving multivariate statistical analysis: Linear regression and classification.” in SDM , 2004
2004
Earlier work this paper cites.
T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: Data mining, inference and prediction,” The Mathematical Intelligencer , vol. 27, no. 2, pp. 83–85, 2005
2005
Earlier work this paper cites.
G. Jagannathan and R. Wright, “Privacy-preserving distributed k-means clustering over arbitrarily partitioned data,” in KDD , 2005
2005
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in TCC , 2006
2006
Earlier work this paper cites.
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig, “Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays,” PLoS Genetics , vol. 4, no. 8, 2008
2008
Earlier work this paper cites.
J. Vaidya, M. Kantarcıoğlu, and C. Clifton, “Privacy-preserving Naive Bayes classification,” VLDB , vol. 17, no. 4, pp. 879–898, 2008
2008
Earlier work this paper cites.
K. Chaudhuri and C. Monteleoni, “Privacy-preserving logistic regression,” in NIPS , 2009
2009
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Master’s thesis, University of Toronto, 2009
2009
Earlier work this paper cites.
S. Sankararaman, G. Obozinski, M. I. Jordan, and E. Halperin, “Genomic privacy and limits of individual detection in a pool,” Nature Genetics , vol. 41, no. 9, pp. 965–967, 2009
2009
Earlier work this paper cites.
C. Dwork and M. Naor, “On the difficulties of disclosure prevention in statistical databases or the case for differential privacy,” J. Privacy and Confidentiality , vol. 2, no. 1, pp. 93–107, 2010
2010
Earlier work this paper cites.
M. Barni, P. Failla, R. Lazzeretti, A. Sadeghi, and T. Schneider, “Privacy-preserving ECG classification with branching programs and neural networks,” Trans. Info. Forensics and Security , vol. 6, no. 2, pp. 452–468, 2011
2011
Earlier work this paper cites.
J. Calandrino, A. Kilzer, A. Narayanan, E. Felten, and V. Shmatikov, ““You might also like:” Privacy risks of collaborative filtering,” in S&P , 2011
2011
Cited alongside, same era.
K. Chaudhuri, C. Monteleoni, and A. Sarwate, “Differentially private empirical risk minimization,” JMLR , vol. 12, pp. 1069–1109, 2011
2011
Cited alongside, same era.
C. Dwork, “Differential privacy,” in Encyclopedia of Cryptography and Security . Springer, 2011, pp. 338–340
2011
Cited alongside, same era.
B. Rubinstein, P. Bartlett, L. Huang, and N. Taft, “Learning in a large function space: Privacy-preserving mechanisms for SVM learning,” J. Privacy and Confidentiality , vol. 4, no. 1, p. 4, 2012
2012
Cited alongside, same era.
M. Wainwright, M. Jordan, and J. Duchi, “Privacy aware learning,” in NIPS , 2012
2012
Cited alongside, same era.
C. Dwork, A. Smith, T. Steinke, J. Ullman, and S. Vadhan, “Robust traceability from trace amounts,” in FOCS , 2015
2015
Later among the works it cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in CCS , 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
2015
Later among the works it cites.
M. J. Kusner, J. R. Gardner, R. Garnett, and K. Q. Weinberger, “Differentially private Bayesian optimization,” in ICML , 2015
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J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett, “Functional mechanism: Regression analysis under differential privacy,” VLDB , vol. 5, no. 11, pp. 1364–1375, 2012
2012
Cited alongside, same era.
R. Bassily, A. Smith, and A. Thakurta, “Private empirical risk minimization: Efficient algorithms and tight error bounds,” in FOCS , 2014
2014
Cited alongside, same era.
J. Bos, K. Lauter, and M. Naehrig, “Private predictive analysis on encrypted medical data,” J. Biomed. Informatics , vol. 50, pp. 234–243, 2014
2014
Cited alongside, same era.
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, “Privacy in pharmacogenetics: An end-to-end case study of personalized Warfarin dosing,” in USENIX Security , 2014
2014
Cited alongside, same era.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” JMLR , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Cited alongside, same era.
2014
Cited alongside, same era.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,” International Journal of Security and Networks , vol. 10, no. 3, pp. 137–150, 2015
2015
Cited alongside, same era.
2015
Later among the works it cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in CCS , 2015
2015
Later among the works it cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in CCS , 2016
2016
Closest in time.
M. Backes, P. Berrang, M. Humbert, and P. Manoharan, “Membership privacy in MicroRNA-based studies,” in CCS , 2016
2016
Closest in time.
M. Hardt, B. Recht, and Y. Singer, “Train faster, generalize better: Stability of stochastic gradient descent,” in ICML , 2016
2016
Closest in time.
F. McSherry, “Statistical inference considered harmful,” https://github.com/frankmcsherry/blog/blob/master/posts/2016-06-14.md , 2016
2016
Closest in time.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction APIs,” in USENIX Security , 2016
2016
Closest in time.
D. Yang, D. Zhang, and B. Qu, “Participatory cultural mapping based on collective behavior data in location-based social networks,” ACM TIST , vol. 7, no. 3, p. 30, 2016
2016
Closest in time.