Secure and Privacy-Preserving Body Sensor Data Collection and Query Scheme

Area of Science:

• Biomedical Engineering
• Computer Science
• Cybersecurity

Background:

• Body sensor networks and smartphones collect real-time personal data, creating valuable datasets.
• Privacy concerns regarding sensitive personal data remain a significant challenge.
• Telemetry interfaces pose security threats to body sensor data.

Purpose of the Study:

• To propose a secure and privacy-preserving body sensor data collection and query scheme (SPCQ) for outsourced cloud computing.
• To address the challenges of protecting sensitive personal information and user query privacy.
• To develop a scheme resistant to security threats from telemetry interfaces.

Main Methods:

• Developed the Secure and Privacy-preserving Body sensor data Collection and Query (SPCQ) scheme.
• Utilized an improved homomorphic encryption technology over a composite order group.
• Proposed a special weighted Euclidean distance contrast (WEDC) algorithm for encrypted multi-dimension vectors.

Main Results:

• SPCQ ensures the confidentiality of sensitive personal data and user queries.
• The scheme provides secure, efficient, and accurate data query services from cloud servers.
• SPCQ demonstrated resistance to various telemetry interface security threats.
• Implementation on embedded devices, smartphones, and laptops showed high efficiency.

Conclusions:

• The proposed SPCQ scheme effectively safeguards sensitive body sensor data and user query privacy in outsourced cloud environments.
• SPCQ offers a practical and efficient solution for secure data collection and querying, validated through real-world implementation and simulations.
• The scheme enhances the trustworthiness of body sensor networks by mitigating privacy risks and security vulnerabilities.