Compressive Sensing based Multi-class Privacy-preserving Cloud Computing

TL;DR

This paper introduces MPCC, a privacy-preserving cloud computing scheme using compressive sensing that enables secure, efficient handling of IoT sensor data with multi-level secrecy and reduced computational costs.

Contribution

It proposes a novel multi-class privacy-preserving scheme leveraging compressive sensing, ensuring data confidentiality and lower computational complexity for IoT applications.

Findings

Achieves two-class secrecy with superuser and semi-authorized user levels.

Reduces computational complexity at sensor and data consumer levels.

Proven secure against ciphertext-only attacks.

Abstract

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.