Next Article in Journal
A Personal, Distributed Exposimeter: Procedure for Design, Calibration, Validation, and Application
Next Article in Special Issue
A Secure Scheme for Distributed Consensus Estimation against Data Falsification in Heterogeneous Wireless Sensor Networks
Previous Article in Journal
A Novel Gas Sensor Based on MgSb2O6 Nanorods to Indicate Variations in Carbon Monoxide and Propane Concentrations
Previous Article in Special Issue
Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(2), 179; doi:10.3390/s16020179

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

Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Academic Editor: Rongxing Lu
Received: 14 October 2015 / Revised: 11 January 2016 / Accepted: 27 January 2016 / Published: 1 February 2016
(This article belongs to the Special Issue Security and Privacy in Sensor Networks)
View Full-Text   |   Download PDF [490 KB, uploaded 1 February 2016]   |  

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. View Full-Text
Keywords: body sensor network; privacy-preserving; data query; outsourced computing body sensor network; privacy-preserving; data query; outsourced computing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhu, H.; Gao, L.; Li, H. Secure and Privacy-Preserving Body Sensor Data Collection and Query Scheme. Sensors 2016, 16, 179.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top