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CPP: A content-aware privacy protection method for location-based service

delete2021-12-23
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PRE
AI
J
Jiabang Liu
X
Xutong Jiang
S
Song Zhang
B
Bowen Liu
W
Wanchun Dou *
DOI:10.1111/exsy.12907delete
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Abstract

Abstract

En 中文
Generally, a location-based service (LBS) often contains the location attribute, content attribute, time-stamp, and range. From the perspective of privacy protection, the location attribute and the content attribute are the key attributes and need to be protected. However, existing privacy protection methods focus excessively on the location attribute and ignore the content attribute contained in the LBS, which discloses the user's private information. In view of this challenge, a content-aware privacy protection method, called the CPP method that considers the content attribute is proposed. Specifically, the CPP method is based on using k-anonymity to generate dummy content attributes to protect the private content. As is shown in an experiment constructed on real-world data, the CPP method can indeed improve the effect of privacy protection.
Keywords:
content privacy
k-anonymity
location-based service
privacy protection
AI Summary

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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Expert Systems cover
Expert Systems
IF:
2.3
Papers:
2.5K
Citations:
3.8K

Organization

N
nanjing university
Scholars:
7.8W
Papers: 5.6W
Citations: 87