SCVS:关于人工智能和边缘云支持隐私保护的智慧城市视频监控服务

IF 3.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Internet of Things Pub Date : 2022-09-06 DOI:10.1145/3542953
Sowmya Myneni, Garima Agrawal, Yuli Deng, Ankur Chowdhary, N. Vadnere, Dijiang Huang
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引用次数: 1

摘要

视频监控系统在许多私立和公立校园、城市建筑和设施中越来越普遍。它们基于从视频传感器捕获的数据提供许多有用的智能校园/城市监控和管理服务。然而,视频监控服务也可能会泄露个人身份信息,特别是被监控的人脸图像;因此,它可能会潜在地侵犯所涉及的人类受试者的隐私。为了解决这一隐私问题,我们引入了一种大规模分布式视频监控服务模型,称为智能城市视频监控(SCVS)。SCVS是一个视频监控数据收集和处理平台,用于识别重要事件,监控,保护和制定智能校园/城市应用决策。在本文中,具体的研究重点是如何在分布式边缘云计算基础设施中识别和匿名化人脸。为了在视频匿名化过程中保护数据的隐私,SCVS采用两步方法:(i)基于参数服务器的分布式机器学习解决方案,确保边缘节点可以交换参数进行基于机器学习的训练。由于数据集不位于集中位置,因此数据隐私和所有权得到了保护和保留。(ii)为了提高机器学习模型的准确性,我们提出了一种异步训练方法,分别为数据所有者和数据用户保护数据和模型隐私。SCVS采用内存加密方式,边缘计算节点以加密形式收集和处理边缘节点内存中的数据。这种方法可以有效地防止诚实但好奇的攻击。性能评估表明,与第5节中介绍的传统集中式计算模型相比,所提出的隐私保护平台是高效有效的。
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SCVS: On AI and Edge Clouds Enabled Privacy-preserved Smart-city Video Surveillance Services
Video surveillance systems are increasingly becoming common in many private and public campuses, city buildings, and facilities. They provide many useful smart campus/city monitoring and management services based on data captured from video sensors. However, the video surveillance services may also breach personally identifiable information, especially human face images being monitored; therefore, it may potentially violate the privacy of human subjects involved. To address this privacy issue, we introduced a large-scale distributed video surveillance service model, called Smart-city Video Surveillance (SCVS). SCVS is a video surveillance data collection and processing platform to identify important events, monitor, protect, and make decisions for smart campus/city applications. In this article, the specific research focus is on how to identify and anonymize human faces in a distributed edge cloud computing infrastructure. To preserve the privacy of data during video anonymization, SCVS utilizes a two-step approach: (i) parameter server-based distributed machine learning solution, which ensures that edge nodes can exchange parameters for machine learning-based training. Since the dataset is not located on a centralized location, the data privacy and ownership are protected and preserved. (ii) To improve the machine learning model’s accuracy, we presented an asynchronous training approach to protect data and model privacy for both data owners and data users, respectively. SCVS adopts an in-memory encryption approach, where edge computing nodes collect and process data in the memory of edge nodes in encrypted form. This approach can effectively prevent honest but curious attacks. The performance evaluation shows the presented privacy protection platform is efficient and effective compared to traditional centralized computing models as presented in Section 5.
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3.70%
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