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

TL;DR

This paper presents a deep learning-based solution for privacy-preserving image matching using encryption techniques, achieving first place in a major challenge focused on protecting personal data in smart city applications.

Contribution

The paper introduces a novel approach combining deep convolutional neural networks with encryption and data augmentation for privacy-preserving image matching.

Findings

Achieved first place in the IEEE Big Data Cup 2022 challenge.

Demonstrated effective matching of encrypted images using deep learning.

Showed robustness of the method across various encryption techniques.

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.