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

Abstract

With the development of body sensor networks and the pervasiveness of smart phones, different types of personal data can be collected in real time by body sensors, and the potential value of massive personal data has attracted considerable interest recently. However, the privacy issues of sensitive personal data are still challenging today. Aiming at these challenges, in this paper, we focus on the threats from telemetry interface and present a secure and privacy-preserving body sensor data collection and query scheme, named SPCQ, for outsourced computing. In the proposed SPCQ scheme, users' personal information is collected by body sensors in different types and converted into multi-dimension data, and each dimension is converted into the form of a number and uploaded to the cloud server, which provides a secure, efficient and accurate data query service, while the privacy of sensitive personal information and users' query data is guaranteed. Specifically, based on an improved homomorphic encryption technology over composite order group, we propose a special weighted Euclidean distance contrast algorithm (WEDC) for multi-dimension vectors over encrypted data. With the SPCQ scheme, the confidentiality of sensitive personal data, the privacy of data users' queries and accurate query service can be achieved in the cloud server. Detailed analysis shows that SPCQ can resist various security threats from telemetry interface. In addition, we also implement SPCQ on an embedded device, smart phone and laptop with a real medical database, and extensive simulation results demonstrate that our proposed SPCQ scheme is highly efficient in terms of computation and communication costs.

Keywords: body sensor network; data query; outsourced computing; privacy-preserving.

Figure 1

Body sensor data collection and query service scenario.

Figure 2

System model under consideration.

Figure 3

Computational overheads of SPCQ and PPRQ. (a) Average running time in DU with different search ranges; (b) average running time in CS with different search ranges.

Figure 4

Computational cost of SPCQ. (a) Computational cost of SU in data collection; (b) computational cost of DU in query generation; (c) computational cost of CS with different search ranges and dimensions; (d) query response time in a real environment.