Personalized DP-SGD using Sampling Mechanisms

• URL: http://arxiv.org/abs/2305.15165v1
• Date: Wed, 24 May 2023 13:56:57 GMT
• Title: Personalized DP-SGD using Sampling Mechanisms
• Authors: Geon Heo, Junseok Seo, and Steven Euijong Whang
• Abstract summary: We extend Differentially Private Gradient Descent (DP-SGD) to support a recent privacy notion called ($Phi$,$Delta$)- Personalized Differential Privacy (($Phi$,$Delta$)- PDP. Our algorithm uses a multi-round personalized sampling mechanism and embeds it within the DP-SGD iteration. Experiments on real datasets show that our algorithm outperforms DP-SGD and simple combinations of DP-SGD with existing PDP mechanisms.
• Score: 5.50042037663784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods supporting privacy, it provides the same level of privacy to all individuals, which may lead to overprotection and low utility. In practice, different users may require different privacy levels, and the model can be improved by using more information about the users with lower privacy requirements. There are also recent works on differential privacy of individuals when using DP-SGD, but they are mostly about individual privacy accounting and do not focus on satisfying different privacy levels. We thus extend DP-SGD to support a recent privacy notion called ($\Phi$,$\Delta$)-Personalized Differential Privacy (($\Phi$,$\Delta$)-PDP), which extends an existing PDP concept called $\Phi$-PDP. Our algorithm uses a multi-round personalized sampling mechanism and embeds it within the DP-SGD iterations. Experiments on real datasets show that our algorithm outperforms DP-SGD and simple combinations of DP-SGD with existing PDP mechanisms in terms of model performance and efficiency due to its embedded sampling mechanism.

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