A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management

• URL: http://arxiv.org/abs/2401.12393v2
• Date: Fri, 27 Sep 2024 14:40:11 GMT
• Title: A Learning-based Declarative Privacy-Preserving Framework for Federated Data Management
• Authors: Hong Guan, Summer Gautier, Rajan Hari Ambrish, Yancheng Wang, Chaowei Xiao, Yingzhen Yang, Jia Zou,
• Abstract summary: We introduce a new privacy-preserving technique that uses a deep learning model trained using Differentially-Private Descent (DP-SGD) algorithm. We then demonstrate a novel declarative privacy-preserving workflow that allows users to specify "what private information to protect" rather than "how to protect"
• Score: 23.847568516724937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is challenging to select the right privacy-preserving mechanism for federated query processing over multiple private data silos. There exist numerous privacy-preserving mechanisms, such as secure multi-party computing (SMC), approximate query processing with differential privacy (DP), combined SMC and DP, DP-based data obfuscation, and federated learning. These mechanisms make different trade-offs among accuracy, privacy, execution efficiency, and storage efficiency. In this work, we first introduce a new 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. We then demonstrate a novel declarative privacy-preserving workflow that allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system relies on a cost model to automatically choose privacy-preserving mechanisms 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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