Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees

Area of Science:

• Health Informatics
• Cloud Computing
• Wireless Sensor Networks

Background:

• Growing health data volume strains Wireless Body Area Sensor Network (WBAN) resources.
• Outsourcing encrypted health data to the cloud is appealing but complicates data aggregation.
• Existing schemes lack comprehensive functionality and address privacy inadequately.

Purpose of the Study:

• To propose a novel privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) for WBANs.
• To address limitations in existing cloud-based health data aggregation methods.
• To enhance data privacy and functionality in outsourced health data management.

Main Methods:

• Developed a differential privacy-based scheme (PMHA-DP) for secure health data aggregation.
• Introduced a new weighted average aggregation function (WAAS).
• Designed a privacy-enhanced aggregation scheme (PAAS) to protect data from cloud servers.
• Implemented a high-accuracy histogram aggregation scheme.
• Ensured fault tolerance within the privacy-preserving framework.

Main Results:

• The proposed PMHA-DP scheme offers enhanced privacy and multiple aggregation functions.
• Achieved accurate histogram aggregation under differential privacy.
• The scheme supports fault tolerance, ensuring data availability.
• Performance evaluation demonstrated reduced communication overhead compared to existing methods.

Conclusions:

• PMHA-DP effectively addresses the challenges of health data aggregation in WBANs.
• The scheme provides a robust solution for secure, private, and efficient cloud-based health data management.
• Offers a multifunctional and fault-tolerant approach superior to current alternatives.