Optimal Differential Privacy Composition for Exponential Mechanisms

Jinshuo Dong, David Durfee, Ryan Rogers
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:2597-2606, 2020.

Abstract

Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP. Exponential mechanism has also become a fundamental building block in private machine learning, e.g. private PCA and hyper-parameter selection. We give explicit formulations of the optimal privacy loss for both the adaptive and non-adaptive composition of exponential mechanism. For the non-adaptive setting in which each mechanism has the same privacy parameter, we give an efficiently computable formulation of the optimal privacy loss. In the adaptive case, we derive a recursive formula and an efficiently computable upper bound. These precise understandings about the problem lead to a 40% saving of the privacy budget in a practical application. Furthermore, the algorithm-specific analysis shows a difference in privacy parameters of adaptive and non-adaptive composition, which was widely believed to not exist based on the evidence from general analysis.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-dong20a, title = {Optimal Differential Privacy Composition for Exponential Mechanisms}, author = {Dong, Jinshuo and Durfee, David and Rogers, Ryan}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {2597--2606}, 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/dong20a/dong20a.pdf}, url = {http://proceedings.mlr.press/v119/dong20a.html}, abstract = {Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP. Exponential mechanism has also become a fundamental building block in private machine learning, e.g. private PCA and hyper-parameter selection. We give explicit formulations of the optimal privacy loss for both the adaptive and non-adaptive composition of exponential mechanism. For the non-adaptive setting in which each mechanism has the same privacy parameter, we give an efficiently computable formulation of the optimal privacy loss. In the adaptive case, we derive a recursive formula and an efficiently computable upper bound. These precise understandings about the problem lead to a 40% saving of the privacy budget in a practical application. Furthermore, the algorithm-specific analysis shows a difference in privacy parameters of adaptive and non-adaptive composition, which was widely believed to not exist based on the evidence from general analysis.} }
Endnote
%0 Conference Paper %T Optimal Differential Privacy Composition for Exponential Mechanisms %A Jinshuo Dong %A David Durfee %A Ryan Rogers %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-dong20a %I PMLR %P 2597--2606 %U http://proceedings.mlr.press/v119/dong20a.html %V 119 %X Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP. Exponential mechanism has also become a fundamental building block in private machine learning, e.g. private PCA and hyper-parameter selection. We give explicit formulations of the optimal privacy loss for both the adaptive and non-adaptive composition of exponential mechanism. For the non-adaptive setting in which each mechanism has the same privacy parameter, we give an efficiently computable formulation of the optimal privacy loss. In the adaptive case, we derive a recursive formula and an efficiently computable upper bound. These precise understandings about the problem lead to a 40% saving of the privacy budget in a practical application. Furthermore, the algorithm-specific analysis shows a difference in privacy parameters of adaptive and non-adaptive composition, which was widely believed to not exist based on the evidence from general analysis.
APA
Dong, J., Durfee, D. & Rogers, R.. (2020). Optimal Differential Privacy Composition for Exponential Mechanisms. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:2597-2606 Available from http://proceedings.mlr.press/v119/dong20a.html.

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