Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry
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Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set. When the training dataset consists of private information, it is natural to use a differentially private ERM algorithm, and this problem has been the subject of a long line of work started with Chaudhuri and Monteleoni 2008. A private ERM algorithm outputs an approximate minimizer of the loss function and its error can be measured as the difference from the optimal value of the loss function. When the constraint set is arbitrary, the required error bounds are fairly well understood BassilyST14. In this work, we show that the geometric properties of the constraint set can be used to derive significantly better results. Specifically, we show that a differentially private version of Mirror Descent leads to error bounds of the form Õ(G_C/n) for a lipschitz loss function, improving on the Õ(√(p)/n) bounds in Bassily, Smith and Thakurta 2014. Here p is the dimensionality of the problem, n is the number of data points in the training set, and G_C denotes the Gaussian width of the constraint set that we optimize over. We show similar improvements for strongly convex functions, and for smooth functions. In addition, we show that when the loss function is Lipschitz with respect to the ℓ_1 norm and C is ℓ_1-bounded, a differentially private version of the Frank-Wolfe algorithm gives error bounds of the form Õ(n^-2/3). This captures the important and common case of sparse linear regression (LASSO), when the data x_i satisfies |x_i|_∞≤ 1 and we optimize over the ℓ_1 ball. We show new lower bounds for this setting, that together with known bounds, imply that all our upper bounds are tight.
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