  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. $\varepsilon=0.5$).
