Initial differential privacy papers
- Robust De-anonymization of Large Sparse Datasets. Arvind Narayanan and Vitaly Shmatikov. IEEE Symposium on Security and Privacy, 2008.
- Differential privacy. Cynthia Dwork. International Colloquium on Automata, Languages and Programming (ICALP), 2006, p. 1-12.
- An Ad Omnia Approach to Defining and Achieving Private Data Analysis. Cynthia Dwork. PinKDD 2007, LNCS 4890, pp. 1-13, 2008.
- Differential Privacy for Statistics: What we Know and What we Want to Learn. Journal of Privacy and Confidentiality (2009) 1, Number 2, pp. 135-154.
- Differential Privacy and Robust Statistics. C. Dwork and J. Lei. Proceedings of the forty-first annual ACM symposium on Theory of computing, pp. 371-380. ACM, 2009.
- Dwork, Cynthia, et al. "On the complexity of differentially private data release: efficient algorithms and hardness results." Proceedings of the forty-first annual ACM symposium on Theory of computing. ACM, 2009.
Machine Learning with Differential Privacy
- The reusable holdout: Preserving validity in adaptive data analysis. Dwork et al. Science, 2014. DOI: 10.1126/science.aaa9375
- K. Chaudhuri, C. Monteleoni. Privacy-preserving logistic regression. NIPS, 2008.
- Machanavajjhala, Ashwin, et al. "Privacy: Theory meets practice on the map."Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on. IEEE, 2008.
- Differentially private recommender systems: building privacy into the Netflix prize contenders. F. McSherry and I. Mironov. KDD, 2009.
- Probabilistic inference and differential privacy. O. Williams and F. McSherry. NIPS 2010.
- Dwork, Cynthia, Guy N. Rothblum, and Salil Vadhan. "Boosting and differential privacy." Foundations of Computer Science (FOCS), 2010 51st Annual IEEE Symposium on. IEEE, 2010.
- Toubiana, Vincent, et al. "Adnostic: Privacy preserving targeted advertising."Proceedings Network and Distributed System Symposium. 2010.
- A simple and practical algorithm for differentially private data release. M. Hardt, K. Ligett, F. McSherry. NIPS 2012.
- Differential privacy based on importance weighting. Z. Ji and C. Elkan. Machine Learning, 2013.
- Privacy Aware Learning. J. Duchi, M. Jordan, M. Wainwright. NIPS 2013.
- 5 Y. Wang, S. Fienberg, A. Smola. Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. ICML, 2015.