The internet of things (IoT) is transforming major industries including but
not limited to healthcare, agriculture, finance, energy, and transportation.
IoT platforms are continually improving with innovations such as the
amalgamation of software-defined networks (SDN) and network function
virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is
becoming popular due to its remarkable accuracy when trained with a massive
amount of data, such as generated by IoT. However, DL algorithms tend to leak
privacy when trained on highly sensitive crowd-sourced data such as medical
data. Existing privacy-preserving DL algorithms rely on the traditional
server-centric approaches requiring high processing powers. We propose a new
local differentially private (LDP) algorithm named LATENT that redesigns the
training process. LATENT enables a data owner to add a randomization layer
before data leave the data owners' devices and reach a potentially untrusted
machine learning service. This feature is achieved by splitting the
architecture of a convolutional neural network (CNN) into three layers: (1)
convolutional module, (2) randomization module, and (3) fully connected module.
Hence, the randomization module can operate as an NFV privacy preservation
service in an SDN-controlled NFV, making LATENT more practical for IoT-driven
cloud-based environments compared to existing approaches. The randomization
module employs a newly proposed LDP protocol named utility enhancing
randomization, which allows LATENT to maintain high utility compared to
existing LDP protocols. Our experimental evaluation of LATENT on convolutional
deep neural networks demonstrates excellent accuracy (e.g. 91%- 96%) with high
model quality even under low privacy budgets (e.g. $\varepsilon=0.5$).

该研究提出了一种名为 LATENT 的本地差分隐私算法，该算法通过将卷积神经网络架构分为卷积模块、随机化模块和全连接模块，并使用新的差分隐私协议实现随机化模块作为网络功能虚拟化隐私保护服务，在边缘 - 云协同中使得保护隐私的深度学习在物联网环境下更加实用。