On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
It addresses privacy-preserving machine learning for irregular data, an incremental advance in differential privacy for non-standard distributions.
The paper tackles the problem of designing differentially private stochastic convex optimization algorithms for heavy-tailed data, which violates assumptions in existing methods, and proposes new methods achieving excess population risks of $ ilde{O}( rac{d^3}{nε^4})$ and improved bounds under various conditions.
In this paper, we consider the problem of designing Differentially Private (DP) algorithms for Stochastic Convex Optimization (SCO) on heavy-tailed data. The irregularity of such data violates some key assumptions used in almost all existing DP-SCO and DP-ERM methods, resulting in failure to provide the DP guarantees. To better understand this type of challenges, we provide in this paper a comprehensive study of DP-SCO under various settings. First, we consider the case where the loss function is strongly convex and smooth. For this case, we propose a method based on the sample-and-aggregate framework, which has an excess population risk of $\tilde{O}(\frac{d^3}{nε^4})$ (after omitting other factors), where $n$ is the sample size and $d$ is the dimensionality of the data. Then, we show that with some additional assumptions on the loss functions, it is possible to reduce the \textit{expected} excess population risk to $\tilde{O}(\frac{ d^2}{ nε^2 })$. To lift these additional conditions, we also provide a gradient smoothing and trimming based scheme to achieve excess population risks of $\tilde{O}(\frac{ d^2}{nε^2})$ and $\tilde{O}(\frac{d^\frac{2}{3}}{(nε^2)^\frac{1}{3}})$ for strongly convex and general convex loss functions, respectively, \textit{with high probability}. Experiments suggest that our algorithms can effectively deal with the challenges caused by data irregularity.