IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning

• URL: http://arxiv.org/abs/2211.11565v1
• Date: Fri, 18 Nov 2022 12:20:40 GMT
• Title: IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning
• Authors: Vrizlynn L. L. Thing
• Abstract summary: 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.
• Score: 1.179179628317559
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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