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许多视频分类应用需要访问用户的个人数据,从而对用户的隐私构成入侵性安全风险。我们提出了一种基于卷积神经网络的单帧方法视频分类的隐私保护实现,该实现允许一方从视频中推断出标签,而无需视频所有者以非加密的方式向其他实体披露他们的视频。类似地,我们的方法消除了分类器所有者以明文形式向外部实体透露其模型参数的要求。为此,我们将现有的用于私有图像分类的安全多方计算(MPC)协议与用于无关单帧选择和跨帧安全标签聚合的新MPC协议相结合。结果是一个端到端的隐私保护视频分类流程。我们在一个私人人类情感识别的应用评估了提出的解决方案。各种安全设置的结果,包括计算各方的诚实和不诚实的大多数配置,以及被动型和主动型对手,表明视频可以以最先进的精确度分类,而且不会泄露敏感用户信息。

https://www.zhuanzhi.ai/paper/7955a3eed16d1e0663383e2abe84594f

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This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques.

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This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques.

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