Differentially private model selection with penalized and constrained likelihood
By Jing Lei, Anne-Sophie Charest, Aleksandra B. Slavkovic, Adam D. Smith, Stephen E. Fienberg. 2016
34 citations recorded by the source corpus.
In statistical disclosure control, the goal of data analysis is twofold: the information released must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning and statistics. It provides a rigorous and strong notion of protection for individuals’ sensitive information. A fundamental question is how to incorporate differential privacy in traditional statistical inference procedures. We study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and we propose two algorithms to do so. We show that our privacy procedures are consistent under essentially the same conditions as the corresponding non‐privacy procedures. We also find that, under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method by using simulation studies and two real data examples.
Subfields: Artificial Intelligence, Statistics and Probability
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jingcheng Liu
- Peter Bloem
- Kunal Talwar
- Giorgio Patrini
- Jonathan Ullman
- Salil P. Vadhan
- Abhradeep Thakurta
- Adam D. Smith
In Statistics and Probability
Citations in the corpus
- Private selection from private candidates (2018, background): “[23] study differentially private model selection for linear models.”
- Three Tools for Practical Differential Privacy (2018, background): “In private training privacy spending accumulates for every trained model [3, 7].”