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Explore a groundbreaking approach to differentially private convex optimization in this IEEE Symposium presentation. Delve into the Approximate Minima Perturbation algorithm, which leverages off-the-shelf optimizers without requiring hyperparameter tuning, making it ideal for practical deployment. Examine the extensive empirical evaluation of state-of-the-art algorithms for differentially private convex optimization across various benchmark and real-world datasets. Gain insights into the open-source implementations of these algorithms and their performance on nine public datasets, including four high-dimensional ones. Learn how to build useful predictive models while guaranteeing the privacy of sensitive data through differential privacy techniques in convex optimization tasks.
