Next Article in Journal
Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors
Next Article in Special Issue
ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition
Previous Article in Journal
A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
Previous Article in Special Issue
Source Authentication for Code Dissemination Supporting Dynamic Packet Size in Wireless Sensor Networks
Open AccessArticle

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

1
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
3
Department of Computer Science, University of Massachusetts at Boston, MA 02125, USA
4
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
5
Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Rongxing Lu
Sensors 2016, 16(9), 1463; https://doi.org/10.3390/s16091463
Received: 25 April 2016 / Revised: 25 August 2016 / Accepted: 26 August 2016 / Published: 10 September 2016
(This article belongs to the Special Issue Security and Privacy in Sensor Networks)
With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one. View Full-Text
Keywords: cloud-assisted WBANs; privacy-enhanced; multifunctional aggregation; health data; fault tolerance; differential privacy cloud-assisted WBANs; privacy-enhanced; multifunctional aggregation; health data; fault tolerance; differential privacy
Show Figures

Figure 1

MDPI and ACS Style

Ren, H.; Li, H.; Liang, X.; He, S.; Dai, Y.; Zhao, L. Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees. Sensors 2016, 16, 1463.

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.

Article Access Map by Country/Region

1
Back to TopTop