论文标题
基于抗压传感的多类隐私云计算
Compressive Sensing based Multi-class Privacy-preserving Cloud Computing
论文作者
论文摘要
在本文中，我们设计了多级隐私$ \ text { - } $保存云计算方案（MPCC），利用压缩传感传感器数据表示和保密数据加密。所提出的方案实现了两级保密性，一种用于超级用户，可以检索确切的传感器数据，另一个用于半授权的用户，他们只能获得诸如均值，差异等统计数据，例如均值，方差等。MPCC方案允许计算昂贵的稀疏信号在云中表现出来，而无需损害云的机密性云服务的数据。通过这种方式，它减轻了由大量的物联网传感器数据引起的数据传输，能源和存储问题，以及对云计算中物联网数据隐私的越来越关注。与最先进的方案相比，我们表明MPCC方案不仅在IoT传感器设备和数据消费者方面具有较低的计算复杂性，而且还被证明是针对仅Ciphertext的攻击而安全的。
In this paper, we design the multi-class privacy$\text{-}$preserving cloud computing scheme (MPCC) leveraging compressive sensing for compact sensor data representation and secrecy for data encryption. The proposed scheme achieves two-class secrecy, one for superuser who can retrieve the exact sensor data, and the other for semi-authorized user who is only able to obtain the statistical data such as mean, variance, etc. MPCC scheme allows computationally expensive sparse signal recovery to be performed at cloud without compromising the confidentiality of data to the cloud service providers. In this way, it mitigates the issues in data transmission, energy and storage caused by massive IoT sensor data as well as the increasing concerns about IoT data privacy in cloud computing. Compared with the state-of-the-art schemes, we show that MPCC scheme not only has lower computational complexity at the IoT sensor device and data consumer, but also is proved to be secure against ciphertext-only attack.
