LDPart: Effective Location-Record Data Publication via Local Differential Privacy
Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targete...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8654185/ |
id |
doaj-281cdfd741d347b9b432e740491c0aa0 |
---|---|
record_format |
Article |
spelling |
doaj-281cdfd741d347b9b432e740491c0aa02021-03-29T22:16:09ZengIEEEIEEE Access2169-35362019-01-017314353144510.1109/ACCESS.2019.28990998654185LDPart: Effective Location-Record Data Publication via Local Differential PrivacyXiangguo Zhao0Yanhui Li1https://orcid.org/0000-0002-4648-9126Ye Yuan2Xin Bi3Guoren Wang4College of Computer Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Computer Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Computer Science and Engineering, Northeastern University, Shenyang, ChinaSino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing, ChinaDriven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart, a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees.https://ieeexplore.ieee.org/document/8654185/Big data privacylocal differential privacylocation-record publication |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiangguo Zhao Yanhui Li Ye Yuan Xin Bi Guoren Wang |
spellingShingle |
Xiangguo Zhao Yanhui Li Ye Yuan Xin Bi Guoren Wang LDPart: Effective Location-Record Data Publication via Local Differential Privacy IEEE Access Big data privacy local differential privacy location-record publication |
author_facet |
Xiangguo Zhao Yanhui Li Ye Yuan Xin Bi Guoren Wang |
author_sort |
Xiangguo Zhao |
title |
LDPart: Effective Location-Record Data Publication via Local Differential Privacy |
title_short |
LDPart: Effective Location-Record Data Publication via Local Differential Privacy |
title_full |
LDPart: Effective Location-Record Data Publication via Local Differential Privacy |
title_fullStr |
LDPart: Effective Location-Record Data Publication via Local Differential Privacy |
title_full_unstemmed |
LDPart: Effective Location-Record Data Publication via Local Differential Privacy |
title_sort |
ldpart: effective location-record data publication via local differential privacy |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart, a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees. |
topic |
Big data privacy local differential privacy location-record publication |
url |
https://ieeexplore.ieee.org/document/8654185/ |
work_keys_str_mv |
AT xiangguozhao ldparteffectivelocationrecorddatapublicationvialocaldifferentialprivacy AT yanhuili ldparteffectivelocationrecorddatapublicationvialocaldifferentialprivacy AT yeyuan ldparteffectivelocationrecorddatapublicationvialocaldifferentialprivacy AT xinbi ldparteffectivelocationrecorddatapublicationvialocaldifferentialprivacy AT guorenwang ldparteffectivelocationrecorddatapublicationvialocaldifferentialprivacy |
_version_ |
1724191930863058944 |