On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data

Di Wang, Hanshen Xiao, Srinivas Devadas, Jinhui Xu
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10081-10091, 2020.

Abstract

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 $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (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 \emph{expected} excess population risk to $\tilde{O}(\frac{ d^2}{ n\epsilon^2 })$. To lift these additional conditions, we also provide a gradient smoothing and trimming based scheme to achieve excess population risks of $\tilde{O}(\frac{ d^2}{n\epsilon^2})$ and $\tilde{O}(\frac{d^\frac{2}{3}}{(n\epsilon^2)^\frac{1}{3}})$ for strongly convex and general convex loss functions, respectively, \emph{with high probability}. Experiments on both synthetic and real-world datasets suggest that our algorithms can effectively deal with the challenges caused by data irregularity.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-wang20y, title = {On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data}, author = {Wang, Di and Xiao, Hanshen and Devadas, Srinivas and Xu, Jinhui}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {10081--10091}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/wang20y/wang20y.pdf}, url = {http://proceedings.mlr.press/v119/wang20y.html}, abstract = {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 $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (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 \emph{expected} excess population risk to $\tilde{O}(\frac{ d^2}{ n\epsilon^2 })$. To lift these additional conditions, we also provide a gradient smoothing and trimming based scheme to achieve excess population risks of $\tilde{O}(\frac{ d^2}{n\epsilon^2})$ and $\tilde{O}(\frac{d^\frac{2}{3}}{(n\epsilon^2)^\frac{1}{3}})$ for strongly convex and general convex loss functions, respectively, \emph{with high probability}. Experiments on both synthetic and real-world datasets suggest that our algorithms can effectively deal with the challenges caused by data irregularity.} }
Endnote
%0 Conference Paper %T On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data %A Di Wang %A Hanshen Xiao %A Srinivas Devadas %A Jinhui Xu %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-wang20y %I PMLR %P 10081--10091 %U http://proceedings.mlr.press/v119/wang20y.html %V 119 %X 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 $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (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 \emph{expected} excess population risk to $\tilde{O}(\frac{ d^2}{ n\epsilon^2 })$. To lift these additional conditions, we also provide a gradient smoothing and trimming based scheme to achieve excess population risks of $\tilde{O}(\frac{ d^2}{n\epsilon^2})$ and $\tilde{O}(\frac{d^\frac{2}{3}}{(n\epsilon^2)^\frac{1}{3}})$ for strongly convex and general convex loss functions, respectively, \emph{with high probability}. Experiments on both synthetic and real-world datasets suggest that our algorithms can effectively deal with the challenges caused by data irregularity.
APA
Wang, D., Xiao, H., Devadas, S. & Xu, J.. (2020). On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:10081-10091 Available from http://proceedings.mlr.press/v119/wang20y.html.

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