Privacy-preserving analytics is designed to protect valuable assets. A common
service provision involves the input data from the client and the model on the
analyst's side. The importance of the privacy preservation is fuelled by legal
obligations and intellectual property concerns. We explore the use case of a
model owner providing an analytic service on customer's private data. No
information about the data shall be revealed to the analyst and no information
about the model shall be leaked to the customer. Current methods involve costs:
accuracy deterioration and computational complexity. The complexity, in turn,
results in a longer processing time, increased requirement on computing
resources, and involves data communication between the client and the server.
In order to deploy such service architecture, we need to evaluate the optimal
setting that fits the constraints. And that is what this paper addresses. In
this work, we enhance an attack detection system based on Convolutional Neural
Networks with privacy-preserving technology based on PriMIA framework that is
initially designed for medical data.

使用 PriMIA 框架，本文在基于卷积神经网络的攻击检测系统上增强了隐私保护技术，以解决隐私保存和数据安全的问题。

使用卷积神经网络的隐私 - 保护入侵检测

Privacy-Preserving Intrusion Detection using Convolutional Neural  Networks

The need for privacy-preserving analytics is higher than ever due to the
severity of privacy risks and to comply with new privacy regulations leading to
an amplified interest in privacy-preserving techniques that try to balance
between privacy and utility. In this work, we present an efficient method for
Text Classification while preserving the privacy of the content using Fully
Homomorphic Encryption (FHE). Our system (named \textbf{Priv}ate \textbf{F}ast
\textbf{T}ext (PrivFT)) performs two tasks: 1) making inference of encrypted
user inputs using a plaintext model and 2) training an effective model using an
encrypted dataset. For inference, we train a supervised model and outline a
system for homomorphic inference on encrypted user inputs with zero loss to
prediction accuracy. In the second part, we show how to train a model using
fully encrypted data to generate an encrypted model. We provide a GPU
implementation of the Cheon-Kim-Kim-Song (CKKS) FHE scheme and compare it with
existing CPU implementations to achieve 1 to 2 orders of magnitude speedup at
various parameter settings. We implement PrivFT in GPUs to achieve a run time
per inference of less than 0.66 seconds. Training on a relatively large
encrypted dataset is more computationally intensive requiring 5.04 days.

本文提出了一种使用全同态加密技术 (Fully Homomorphic Encryption, FHE) 来实现隐私保护文本分类的高效方法，旨在在不泄漏数据隐私的前提下，保持预测准确性。

PrivFT: 具有同态加密的私密快速文本分类

PrivFT: Private and Fast Text Classification with Homomorphic Encryption

Internet of Things (IoT) devices and applications are being deployed in our
homes and workplaces. These devices often rely on continuous data collection to
feed machine learning models. However, this approach introduces several privacy
and efficiency challenges, as the service operator can perform unwanted
inferences on the available data. Recently, advances in edge processing have
paved the way for more efficient, and private, data processing at the source
for simple tasks and lighter models, though they remain a challenge for larger,
and more complicated models. In this paper, we present a hybrid approach for
breaking down large, complex deep neural networks for cooperative,
privacy-preserving analytics. To this end, instead of performing the whole
operation on the cloud, we let an IoT device to run the initial layers of the
neural network, and then send the output to the cloud to feed the remaining
layers and produce the final result. In order to ensure that the user's device
contains no extra information except what is necessary for the main task and
preventing any secondary inference on the data, we introduce Siamese
fine-tuning. We evaluate the privacy benefits of this approach based on the
information exposed to the cloud service. We also assess the local inference
cost of different layers on a modern handset. Our evaluations show that by
using Siamese fine-tuning and at a small processing cost, we can greatly reduce
the level of unnecessary, potentially sensitive information in the personal
data, and thus achieving the desired trade-off between utility, privacy, and
performance.

本篇论文提出了一种用于合作、隐私保护分析的深度神经网络混合方法，使用边缘处理的思想，在保持效益的同时，有效减少了不必要、潜在敏感信息的级别。利用 Siamese 微调技术，可以确保用户设备只带有主任务所需的必要信息，并防止数据的二次推断。