TY - JOUR

T1 - Multi-class privacy-preserving cloud computing based on compressive sensing for IoT

AU - Kuldeep, Gajraj

AU - Zhang, Qi

PY - 2022/5

Y1 - 2022/5

N2 - In this paper, we design the multi-class privacy-preserving cloud computing scheme (MPCC) leveraging compressive sensing for compact sensor data representation and secrecy for data encryption. Three variants of the MPCC scheme are proposed, realizing statistical decryption for smart meters, and data anonymization for images and electrocardiogram signals. The proposed MPCC variants achieve two-class secrecy, one for the superuser who can retrieve the exact sensor data and the other for the semi-authorized user, who can only obtain the statistical data such as mean, variance, etc., or the signals without sensitive part of information, depending on which variant of the MPCC is used. MPCC scheme allows computationally expensive sparse signal recovery to be performed at the cloud without compromising data confidentiality 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. We show that the MPCC scheme has lower computational complexity at the IoT sensor device and data end-users than the state-of-the-art schemes. Experimental results on three datasets, i.e., smart meter, electrocardiogram, and images, demonstrate the MPCC’s performance in statistical decryption and data anonymization.

AB - In this paper, we design the multi-class privacy-preserving cloud computing scheme (MPCC) leveraging compressive sensing for compact sensor data representation and secrecy for data encryption. Three variants of the MPCC scheme are proposed, realizing statistical decryption for smart meters, and data anonymization for images and electrocardiogram signals. The proposed MPCC variants achieve two-class secrecy, one for the superuser who can retrieve the exact sensor data and the other for the semi-authorized user, who can only obtain the statistical data such as mean, variance, etc., or the signals without sensitive part of information, depending on which variant of the MPCC is used. MPCC scheme allows computationally expensive sparse signal recovery to be performed at the cloud without compromising data confidentiality 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. We show that the MPCC scheme has lower computational complexity at the IoT sensor device and data end-users than the state-of-the-art schemes. Experimental results on three datasets, i.e., smart meter, electrocardiogram, and images, demonstrate the MPCC’s performance in statistical decryption and data anonymization.

U2 - 10.1016/j.jisa.2022.103139

DO - 10.1016/j.jisa.2022.103139

M3 - Journal article

VL - 66

JO - Journal of Information Security and Applications

JF - Journal of Information Security and Applications

SN - 2214-2126

M1 - 103139

ER -