A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems
In the applications for mobile sensing, the trustworthy of sensed data should be put on the first place. The identification of participants can ensure data trustworthy but will reveal the privacy of the participants to a great extent. In this paper, we propose a privacy-preserving identification mec...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8283702/ |
id |
doaj-905fba9fcaf54542a50a6b9b41b1b03e |
---|---|
record_format |
Article |
spelling |
doaj-905fba9fcaf54542a50a6b9b41b1b03e2021-03-29T20:47:59ZengIEEEIEEE Access2169-35362018-01-016154571546710.1109/ACCESS.2018.28031298283702A Privacy-Preserving Identification Mechanism for Mobile Sensing SystemsXiaoguang Niu0https://orcid.org/0000-0003-4252-3291Qiongzan Ye1Yihao Zhang2Dengpan Ye3School of Computer Science, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaSchool of Computer Science, Wuhan University, Wuhan, ChinaIn the applications for mobile sensing, the trustworthy of sensed data should be put on the first place. The identification of participants can ensure data trustworthy but will reveal the privacy of the participants to a great extent. In this paper, we propose a privacy-preserving identification mechanism for mobile sensing systems to select sensed data dynamically to protect participant's sensitive information. It solves the contradiction between “privacy protection”and “identification”. It divides data privacy sensitivity of the data sensed from the task that participants attended, allowing participants to define their own privacy sensitivity, then selects sensed data dynamically and uses differential privacy to process the data with high privacy sensitivity. It can not only protect participants' privacy, but also identify participants' IDs. In order to achieve identification, a two-layer neural network model is used to train and learn the participant's style of action and generate an identity feature database. The experimental results show that the proposed mechanism can provide a trustworthy platform for mobile sensing systems.https://ieeexplore.ieee.org/document/8283702/Mobile sensing systemtrustworthyprivacy preservingdifferential privacyidentification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoguang Niu Qiongzan Ye Yihao Zhang Dengpan Ye |
spellingShingle |
Xiaoguang Niu Qiongzan Ye Yihao Zhang Dengpan Ye A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems IEEE Access Mobile sensing system trustworthy privacy preserving differential privacy identification |
author_facet |
Xiaoguang Niu Qiongzan Ye Yihao Zhang Dengpan Ye |
author_sort |
Xiaoguang Niu |
title |
A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems |
title_short |
A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems |
title_full |
A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems |
title_fullStr |
A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems |
title_full_unstemmed |
A Privacy-Preserving Identification Mechanism for Mobile Sensing Systems |
title_sort |
privacy-preserving identification mechanism for mobile sensing systems |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
In the applications for mobile sensing, the trustworthy of sensed data should be put on the first place. The identification of participants can ensure data trustworthy but will reveal the privacy of the participants to a great extent. In this paper, we propose a privacy-preserving identification mechanism for mobile sensing systems to select sensed data dynamically to protect participant's sensitive information. It solves the contradiction between “privacy protection”and “identification”. It divides data privacy sensitivity of the data sensed from the task that participants attended, allowing participants to define their own privacy sensitivity, then selects sensed data dynamically and uses differential privacy to process the data with high privacy sensitivity. It can not only protect participants' privacy, but also identify participants' IDs. In order to achieve identification, a two-layer neural network model is used to train and learn the participant's style of action and generate an identity feature database. The experimental results show that the proposed mechanism can provide a trustworthy platform for mobile sensing systems. |
topic |
Mobile sensing system trustworthy privacy preserving differential privacy identification |
url |
https://ieeexplore.ieee.org/document/8283702/ |
work_keys_str_mv |
AT xiaoguangniu aprivacypreservingidentificationmechanismformobilesensingsystems AT qiongzanye aprivacypreservingidentificationmechanismformobilesensingsystems AT yihaozhang aprivacypreservingidentificationmechanismformobilesensingsystems AT dengpanye aprivacypreservingidentificationmechanismformobilesensingsystems AT xiaoguangniu privacypreservingidentificationmechanismformobilesensingsystems AT qiongzanye privacypreservingidentificationmechanismformobilesensingsystems AT yihaozhang privacypreservingidentificationmechanismformobilesensingsystems AT dengpanye privacypreservingidentificationmechanismformobilesensingsystems |
_version_ |
1724194168128929792 |