Anonymizing mobility data using semantic cloaking

doi:10.1016/j.pmcj.2015.10.013

2016-06-01

PRE

Omer Barak

Gabriella Cohen

Eran Toch *

DOI：10.1016/j.pmcj.2015.10.013

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摘要

En 中文

The prevalence of mobile phones has led to an explosion in the amounts of human mobility data stored in the cloud. It has been shown that seemingly anonymized location datasets are highly susceptible for re-identification, and may not provide enough privacy protection. In this paper we quantitatively show how semantic cloaking, the application of semantic labeling to achieve anonymization, can improve the privacy of a mobility dataset for use cases where location coordinates can be replaced by semantic categories. We develop a semantic labeling framework, apply it and evaluate it using the dataset uniqueness (e) measure. Our experiments show an improvement in uniqueness ranging between two- and twenty two-fold in comparison to the original, naively anonymized, dataset. (C) 2015 Elsevier B.V. All rights reserved.

Keyword:

Location privacy

Human mobility

Semantic labeling

Semantic cloaking

k-anonymity

期刊

Pervasive and Mobile Computing

IF:

3.5

论文数:

1.5K

被引数:

2.2K

机构

Tel Aviv University

学者数:

3.7W

论文数: 3.0W

被引数: 3.6W

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