A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data

Fawza A. Al-Zumia; Yuan Tian; Mznah Al-Rodhaan; Fawza A. Al-Zumia; Yuan Tian; Mznah Al-Rodhaan

doi:10.3934/mbe.2021373

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 7539-7560. doi: 10.3934/mbe.2021373

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A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data

1.
Computer Science Department, King Saud University, Riyadh, Kingdom of Saudi Arabia
2.
Nanjing Institute of Technology, Nanjing, China

Academic Editor：Yang Kuang

Received: 10 April 2021 Accepted: 12 August 2021 Published: 02 September 2021

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.
- lightweight data,
- fault tolerance,
- privacy,
- data aggregation,
- cloud computing
Citation: Fawza A. Al-Zumia, Yuan Tian, Mznah Al-Rodhaan. A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7539-7560. doi: 10.3934/mbe.2021373

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Abstract

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.

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