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.

该研究提出了一种端到端的工作流程，用于自动化隐私保护技术，通过使用差分隐私随机梯度下降（DP-SGD）算法训练的深度学习模型替换实际数据的部分以回答查询，以平衡联邦查询处理中的隐私和准确性。

基于学习的声明性隐私保护联邦数据管理框架

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

Facial recognition technologies are implemented in many areas, including but
not limited to, citizen surveillance, crime control, activity monitoring, and
facial expression evaluation. However, processing biometric information is a
resource-intensive task that often involves third-party servers, which can be
accessed by adversaries with malicious intent. Biometric information delivered
to untrusted third-party servers in an uncontrolled manner can be considered a
significant privacy leak (i.e. uncontrolled information release) as biometrics
can be correlated with sensitive data such as healthcare or financial records.
In this paper, we propose a privacy-preserving technique for "controlled
information release", where we disguise an original face image and prevent
leakage of the biometric features while identifying a person. We introduce a
new privacy-preserving face recognition protocol named PEEP (Privacy using
EigEnface Perturbation) that utilizes local differential privacy. PEEP applies
perturbation to Eigenfaces utilizing differential privacy and stores only the
perturbed data in the third-party servers to run a standard Eigenface
recognition algorithm. As a result, the trained model will not be vulnerable to
privacy attacks such as membership inference and model memorization attacks.
Our experiments show that PEEP exhibits a classification accuracy of around 70%
- 90% under standard privacy settings.

本文提出了一种隐私保护技术，即 PEEP，该技术可对面部图像进行扰动以防止生物特征泄漏，并利用局部差分隐私的方法在第三方服务器上运行 Eigenface 识别算法。实验结果表明，PEEP 在标准隐私设置下的分类准确率约为 70％-90％。