With the rapid development of the Internet of Things, a large number of mobile devices participate in the perception and aggregation of data, outsourcing and storing massive data on various cloud platforms. Thus, a new data perception and privacy protection model based on mobile group intelligence perception and cloud computing is required. The existing work on data security and privacy protection mainly focuses on the independent link of data collection, aggregation, and services, which lacks holistic consideration for the different service requirements. To end this issue, this article comprehensively integrates the security and privacy protection requirements of various stages in the mobile group intelligence perception for data collection, aggregation, and service from a global perspective. Aiming at data privacy and security issues in the existing mobile group intelligence perception system, and the difficulty in guaranteeing the quantity and quality of data at the same time, a mobile group intelligence perception mechanism oriented to user privacy and data security is proposed. This mechanism uses deep learning as its core algorithm to handle big data cases. It can provide authenticity and reliability guarantees for the subsequent data application on the premise of protecting user privacy. Experimental results prove that the mechanism proposed in this article meets the security requirements, and its user-end computing overhead is small.

中文翻译：

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物联网中面向用户隐私和数据安全的基于深度学习的移动群智能感知机制

随着物联网的快速发展，大量的移动设备参与到数据的感知和聚合中，将海量数据外包和存储在各种云平台上。因此，需要一种基于移动群体智能感知和云计算的新型数据感知和隐私保护模型。现有的数据安全和隐私保护工作主要集中在数据采集、聚合和服务的独立环节，缺乏对不同服务需求的整体考虑。为结束本期，本文从全局角度综合整合移动群智感知各个阶段对数据采集、聚合、服务的安全和隐私保护需求。针对现有移动群体智能感知系统中的数据隐私和安全问题，同时难以保证数据的数量和质量，提出一种面向用户隐私和数据安全的移动群体智能感知机制。该机制以深度学习为核心算法处理大数据案例。可以在保护用户隐私的前提下，为后续数据应用提供真实可靠的保障。实验结果证明，本文提出的机制满足安全要求，用户端计算开销小。提出一种面向用户隐私和数据安全的移动群体智能感知机制。该机制以深度学习为核心算法处理大数据案例。可以在保护用户隐私的前提下，为后续数据应用提供真实可靠的保障。实验结果证明，本文提出的机制满足安全要求，用户端计算开销小。提出一种面向用户隐私和数据安全的移动群体智能感知机制。该机制以深度学习为核心算法处理大数据案例。可以在保护用户隐私的前提下，为后续数据应用提供真实可靠的保障。实验结果证明，本文提出的机制满足安全要求，用户端计算开销小。