Further Reading
- Chaudhuri,K., and Monteleoni,C. 2009. Privacy-preserving logistic regression. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, 289–296. Red Hook, NY: Curran Associates, Inc.
- Dwork,C.,McSherry,F.,Nissim,K., and Smith,A. 2006. Calibrating Noise to Sensitivity in Private Data Analysis, 265–284. Berlin and Heidelberg, Germany: Springer Berlin Heidelberg.
- Dwork,C.,Roth,A., et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9(3–4):211–407.
- Hall,R.,Rinaldo,A., and Wasserman,L. 2013. Differential privacy for functions and functional data. Journal of Machine Learning Research 14(1):703–727.
- Mirshani,A.,Reimherr,M., and Slavkovic,A. 2019. Formal privacy for functional data with gaussian perturbations. In International Conference on Machine Learning, 4,595–4,604.
- Smith,M.,Álvarez,M.,Zwiessele,M., and Lawrence,N.D. 2018. Differentially private regression with gaussian processes. In International Conference on Artificial Intelligence and Statistics 1,195–1,203.
- Wasserman,L. 2006. All of nonparametric statistics. Berlin, Heidelberg, and Dordrecht, Germany; and New York: Springer Science & Business Media.