期刊
DIGITAL COMMUNICATIONS AND NETWORKS
卷 7, 期 3, 页码 445-452出版社
KEAI PUBLISHING LTD
DOI: 10.1016/j.dcan.2020.10.001
关键词
Online health monitoring; Trajectory privacy; User trajectory model; Aggregated data; Uniqueness
资金
- National Natural Science Foundation of China [61871062, 61771082]
- Natural Science Foundation of Chongqing of China [cstc2013jcyjA40066]
- Program for Innovation Team Building at Institutions of Higher Education in Chongqing [CXTDX201601020]
- Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201801316]
- Key Industrial Technology Development Project of Chongqing of China Development and Reform Commission [2018148208]
- Innovation and Entrepreneurship Demonstration Team of Yingcai Program of Chongqing of China [CQYC201903167]
A large amount of sensitive personal data is collected by online health monitoring applications, with user trajectories potentially becoming the key to re-identify users. The User Trajectory Model (UTM) is proposed to mathematically derive statistical characteristics of trajectory uniqueness, validating its effectiveness in privacy risk evaluation.
A huge amount of sensitive personal data is being collected by various online health monitoring applications. Although the data is anonymous, the personal trajectories (e.g., the chronological access records of small cells) could become the anchor of linkage attacks to re-identify the users. Focusing on trajectory privacy in online health monitoring, we propose the User Trajectory Model (UTM), a generic trajectory re-identification risk predicting model to reveal the underlying relationship between trajectory uniqueness and aggregated data (e.g., number of individuals covered by each small cell), and using the parameter combination of aggregated data to further mathematically derive the statistical characteristics of uniqueness (i.e., the expectation and the variance). Eventually, exhaustive simulations validate the effectiveness of the UTM in privacy risk evaluation, confirm our theoretical deductions and present counter-intuitive insights.
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