Understand
Since its conception in 2006, differential privacy has emerged as the de-facto standard in data privacy, owing to its robust mathematical guarantees, generalised applicability and rich body of literature.
- Over the years, researchers have studied differential privacy and its applicability to an ever-widening field of topics.
- Mechanisms have been created to optimise the process of achieving differential privacy, for various data types and scenarios.
- Until this work however, all previous work on differential privacy has been conducted on a ad-hoc basis, without a single, unifying codebase to implement results.
Built on
PEP 8 – style guide for Python code
van Rossum, G., Warsaw, B., and Coghlan, N · 2001
Earlier work this paper cites.
Differential privacy
Dwork, C · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
McSherry, F., and Talwar, K · 2007
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
Earlier work this paper cites.
Similar
The NumPy array: A structure for efficient numerical computation
van der Walt, S., Colbert, S. C., and Varoquaux, G · 2011
Cited alongside, same era.
Universally utility-maximizing privacy mechanisms
Ghosh, A., Roughgarden, T., and Sundararajan, M · 2012
Cited alongside, same era.
Differentially private naïve Bayes classification
Vaidya, J., Shafiq, B., Basu, A., and Hong, Y · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Dwork, C., and Roth, A · 2014
Cited alongside, same era.
The staircase mechanism in differential privacy
Geng, Q., Kairouz, P., Oh, S., and Viswanath, P · 2015
Cited alongside, same era.
Differential privacy in metric spaces: Numerical, categorical and functional data under the one roof
Holohan, N., Leith, D. J., and Mason, O · 2015
Cited alongside, same era.
Then
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Later among the works it cites.
Differentially private k k -means clustering
Su, D., Cao, J., Li, N., Bertino, E., and Jin, H · 2016
Later among the works it cites.
Optimal differentially private mechanisms for randomised response
Holohan, N., Leith, D. J., and Mason, O · 2017
Later among the works it cites.
Improving the Gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Balle, B., and Wang, Y · 2018
Later among the works it cites.
Privacy and utility tradeoff in approximate differential privacy
Geng, Q., Ding, W., Guo, R., and Kumar, S · 2018
Later among the works it cites.
The bounded Laplace mechanism in differential privacy
Holohan, N., Antonatos, S., Braghin, S., and Mac Aonghusa, P · 2018
Later among the works it cites.
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