A Learning-based Declarative Privacy-Preserving Framework for Federated
Data Management
- URL: http://arxiv.org/abs/2401.12393v1
- Date: Mon, 22 Jan 2024 22:50:59 GMT
- Title: A Learning-based Declarative Privacy-Preserving Framework for Federated
Data Management
- Authors: Hong Guan, Summer Gautier, Deepti Gupta, Rajan Hari Ambrish, Yancheng
Wang, Harsha Lakamsani, Dhanush Giriyan, Saajan Maslanka, Chaowei Xiao,
Yingzhen Yang, Jia Zou
- Abstract summary: We will demonstrate an end-to-end workflow for automating privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Gradient Descent (DP-SGD) algorithm.
Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect"
- Score: 21.324702503929554
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: It is challenging to balance the privacy and accuracy for federated query
processing over multiple private data silos. In this work, we will demonstrate
an end-to-end workflow for automating an emerging privacy-preserving technique
that uses a deep learning model trained using the Differentially-Private
Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual
data to answer a query. Our proposed novel declarative privacy-preserving
workflow allows users to specify "what private information to protect" rather
than "how to protect". Under the hood, the system automatically chooses
query-model transformation plans as well as hyper-parameters. At the same time,
the proposed workflow also allows human experts to review and tune the selected
privacy-preserving mechanism for audit/compliance, and optimization purposes.
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