MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for
  Multi-task Learning
Md Adnan Arefeen Zhouyu Li Md Yusuf Sarwar Uddin Anupam Das
With the growth of computer vision applications, deep learning, and edge
computing contribute to ensuring practical collaborative intelligence (CI) by
distributing the workload among edge devices and the cloud. However, running
separate single-task models on edge devices is inefficient regarding the
required computational resource and time. In this context, multi-task learning
allows leveraging a single deep learning model for performing multiple tasks,
such as semantic segmentation and depth estimation on incoming video frames.
This single processing pipeline generates common deep features that are shared
among multi-task modules. However, in a collaborative intelligence scenario,
generating common deep features has two major issues. First, the deep features
may inadvertently contain input information exposed to the downstream modules
(violating input privacy). Second, the generated universal features expose a
piece of collective information than what is intended for a certain task, in
which features for one task can be utilized to perform another task (violating
task privacy). This paper proposes a novel deep learning-based
privacy-cognizant feature generation process called MetaMorphosis that limits
inference capability to specific tasks at hand. To achieve this, we propose a
channel squeeze-excitation based feature metamorphosis module, Cross-SEC, to
achieve distinct attention of all tasks and a de-correlation loss function with
differential-privacy to train a deep learning model that produces distinct
privacy-aware features as an output for the respective tasks. With extensive
experimentation on four datasets consisting of diverse images related to scene
understanding and facial attributes, we show that MetaMorphosis outperforms
recent adversarial learning and universal feature generation methods by
guaranteeing privacy requirements in an efficient way for image and video
analytics.