  Smart sensors, devices and systems deployed in smart cities have brought
improved physical protections to their citizens. Enhanced crime prevention, and
fire and life safety protection are achieved through these technologies that
perform motion detection, threat and actors profiling, and real-time alerts.
However, an important requirement in these increasingly prevalent deployments
is the preservation of privacy and enforcement of protection of personal
identifiable information. Thus, strong encryption and anonymization techniques
should be applied to the collected data. In this IEEE Big Data Cup 2022
challenge, different masking, encoding and homomorphic encryption techniques
were applied to the images to protect the privacy of their contents.
Participants are required to develop detection solutions to perform privacy
preserving matching of these images. In this paper, we describe our solution
which is based on state-of-the-art deep convolutional neural networks and
various data augmentation techniques. Our solution achieved 1st place at the
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images
Challenge.
