Compressive Sensing based Multi-class Privacy-preserving Cloud Computing
This addresses privacy and efficiency issues in IoT data transmission and cloud computing, but it is incremental as it builds on existing compressive sensing and encryption methods.
The paper tackles the problem of privacy-preserving cloud computing for IoT sensor data by proposing a multi-class scheme (MPCC) that uses compressive sensing for compact representation and encryption, achieving two-class secrecy for different user types and reducing computational complexity at IoT devices and data consumers while proving security against ciphertext-only attacks.
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.