Abstract
Privacy protection in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. The perceptual layer of IoTs suffers the most significant privacy disclosing because of the limitation of hardware resources. Data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. Therefore, in this paper we derive an innovative and passive method called Horizontal Hierarchy Slicing (HHS) method to detect the existence of unknown wireless devices which could result negative means to the privacy. PAM algorithm is used to cluster the HHS curves and analyze whether unknown wireless devices exist in the communicating environment. Link Quality Indicator data are utilized as the network parameters in this paper. The simulation results show their effectiveness in privacy protection.
| Original language | English |
|---|---|
| Title of host publication | 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings |
| Publisher | Association for Computing Machinery |
| Pages | 335-339 |
| Number of pages | 5 |
| Volume | Part F126325 |
| ISBN (Electronic) | 9781450348072 |
| DOIs | |
| Publication status | Published - 2016 Nov 28 |
| Event | 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Singapore, Singapore Duration: 2016 Nov 28 → 2016 Nov 30 |
Other
| Other | 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 16/11/28 → 16/11/30 |
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Keywords
- Horizontal Hierarchy Slicing
- Internet of Things
- Link Quality Indicator
- Privacy protection
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
A passive means based privacy protection method for the perceptual layer of IoTs. / Li, Xiaoyu; Yoshie, Osamu; Huang, Daoping.
18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings. Vol. Part F126325 Association for Computing Machinery, 2016. p. 335-339.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A passive means based privacy protection method for the perceptual layer of IoTs
AU - Li, Xiaoyu
AU - Yoshie, Osamu
AU - Huang, Daoping
PY - 2016/11/28
Y1 - 2016/11/28
N2 - Privacy protection in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. The perceptual layer of IoTs suffers the most significant privacy disclosing because of the limitation of hardware resources. Data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. Therefore, in this paper we derive an innovative and passive method called Horizontal Hierarchy Slicing (HHS) method to detect the existence of unknown wireless devices which could result negative means to the privacy. PAM algorithm is used to cluster the HHS curves and analyze whether unknown wireless devices exist in the communicating environment. Link Quality Indicator data are utilized as the network parameters in this paper. The simulation results show their effectiveness in privacy protection.
AB - Privacy protection in Internet of Things (IoTs) has long been the topic of extensive research in the last decade. The perceptual layer of IoTs suffers the most significant privacy disclosing because of the limitation of hardware resources. Data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. Therefore, in this paper we derive an innovative and passive method called Horizontal Hierarchy Slicing (HHS) method to detect the existence of unknown wireless devices which could result negative means to the privacy. PAM algorithm is used to cluster the HHS curves and analyze whether unknown wireless devices exist in the communicating environment. Link Quality Indicator data are utilized as the network parameters in this paper. The simulation results show their effectiveness in privacy protection.
KW - Horizontal Hierarchy Slicing
KW - Internet of Things
KW - Link Quality Indicator
KW - Privacy protection
UR - http://www.scopus.com/inward/record.url?scp=85014888284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014888284&partnerID=8YFLogxK
U2 - 10.1145/3011141.3011153
DO - 10.1145/3011141.3011153
M3 - Conference contribution
AN - SCOPUS:85014888284
VL - Part F126325
SP - 335
EP - 339
BT - 18th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2016 - Proceedings
PB - Association for Computing Machinery
ER -