  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.
