Preserving privacy for moving objects data mining

Shen Shyang Ho

doi:10.1109/ISI.2012.6284198

Preserving privacy for moving objects data mining

Shen Shyang Ho

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (∈, δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (∈, δ)-differential privacy mechanism with the standard ∈-differential privacy mechanism.

Original language	English (US)
Title of host publication	ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publication	Cyberspace, Border, and Immigration Securities
Pages	135-137
Number of pages	3
DOIs	https://doi.org/10.1109/ISI.2012.6284198
State	Published - Oct 17 2012
Externally published	Yes
Event	2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 - Washington, DC, United States Duration: Jun 11 2012 → Jun 14 2012

Other

Other	2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012
Country/Territory	United States
City	Washington, DC
Period	6/11/12 → 6/14/12

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems

Access to Document

10.1109/ISI.2012.6284198

Cite this

@inproceedings{32606efe8e8e4968a38ca2a67a1db8bb,

title = "Preserving privacy for moving objects data mining",

abstract = "The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (∈, δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (∈, δ)-differential privacy mechanism with the standard ∈-differential privacy mechanism.",

author = "Ho, {Shen Shyang}",

year = "2012",

month = oct,

day = "17",

doi = "10.1109/ISI.2012.6284198",

language = "English (US)",

isbn = "9781467321037",

pages = "135--137",

booktitle = "ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics",

note = "2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012 ; Conference date: 11-06-2012 Through 14-06-2012",

}

Ho, SS 2012, Preserving privacy for moving objects data mining. in ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities., 6284198, pp. 135-137, 2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012, Washington, DC, United States, 6/11/12. https://doi.org/10.1109/ISI.2012.6284198

TY - GEN

T1 - Preserving privacy for moving objects data mining

AU - Ho, Shen Shyang

PY - 2012/10/17

Y1 - 2012/10/17

N2 - The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (∈, δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (∈, δ)-differential privacy mechanism with the standard ∈-differential privacy mechanism.

AB - The prevalence of mobile devices with geopositioning capability has resulted in the rapid growth in the amount of moving object trajectories. These data have been collected and analyzed for both commercial (e.g., recommendation system) and security (e.g. surveillance and monitoring system) purposes. One needs to ensure the privacy of these raw trajectory data and the derived knowledge by not disclosing or releasing them to adversary. In this paper, we propose a practical implementation of a (∈, δ)-differentially private mechanism for moving objects data mining; in particular, we apply it to the frequent location pattern mining algorithm. Experimental results on the real-world GeoLife dataset are used to compare the performance of the (∈, δ)-differential privacy mechanism with the standard ∈-differential privacy mechanism.

UR - http://www.scopus.com/inward/record.url?scp=84867364687&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867364687&partnerID=8YFLogxK

U2 - 10.1109/ISI.2012.6284198

DO - 10.1109/ISI.2012.6284198

M3 - Conference contribution

AN - SCOPUS:84867364687

SN - 9781467321037

SP - 135

EP - 137

BT - ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics

T2 - 2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012

Y2 - 11 June 2012 through 14 June 2012

ER -

Preserving privacy for moving objects data mining

Abstract

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this