Further Reading
- Bun,Mark,Ullman,Jonathan, and Vadhan,Salil. 2014. Fingerprinting codes and the price of approximate differential privacy. STOC 2014, 1–10. ACM.
- Cai,T. Tony,Wang,Yichen, and Zhang,Linjun. 2019. The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy. arXiv: 1902.04495.
- Dwork,Cynthia, and Roth,Aaron. 2014. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9(3–4):211–407.
- Dwork,Cynthia,Feldman,Vitaly,Hardt,Moritz,Pitassi,Toniann,Reingold,Omer, and Roth,Aaron. 2014. Preserving statistical validity in adaptive data analysis. CoRR, abs/1411.2664 Extended abstract in STOC 2015.
- Dwork,Cynthia,Feldman,Vitaly,Hardt,Moritz,Pitassi,Toniann,Reingold,Omer, and Roth,Aaron. 2015. The reusable holdout: Preserving validity in adaptive data analysis. Science 349, 636–638.
- Li,Yinan, and Liu,Fang. 2017. Whiteout: Gaussian adaptive noise regularization in feedforward neural networks. arXiv:1612.01490.
- Li,Yinan,Liu,Xiao, and Liu,Fang. 2018. PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models. arXiv: 1810.04851.
- Yu,Fei,Fienberg,Stephen E.,Slavković,Aleksandra B., and Uhler,Caroline. 2014. Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of Biomedical Informatics 50, 133–141.