IEEE 大型数据杯 2022 : 将加密图像与深学习匹配的隐私保护 (IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning)

from arxiv, Keywords: privacy preservation, privacy enhancing, masking, encoding, homomorphic encryption, deep learning, convolutional neural networks

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.

翻译：在智能城市部署的智能传感器、装置和系统改善了公民的人身保护。通过这些能够进行运动探测、威胁和行为者特征分析以及实时警报的技术,加强了预防犯罪以及防火和生命安全保护。然而,在这些日益流行的部署中,一个重要的要求是保护隐私和强制执行个人可识别信息的保护。因此,对收集的数据应当采用强有力的加密和匿名技术。在IEEEE大数据杯2022年的这次挑战中,对图像应用了不同的遮蔽、编码和同色加密技术以保护其内容的隐私。参与者需要制定探测解决方案,以维护这些图像的隐私匹配。在本文件中,我们描述了我们基于最先进的深层革命神经网络和各种数据增强技术的解决办法。我们的解决方案在IEEE大数据杯2022年实现了第1个位置:加密图像的隐私匹配挑战。