Contact Tracing With District-Based Trajectories

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Kiki Adhinugraha, W. Rahayu, Nasser Allheeib
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引用次数: 0

Abstract

Identifying the places an infected person has visited during a virus incubation time in order to conduct contact tracing is currently done using manual interviews since proximity-based contact tracing methods do not store geolocation information due to privacy concerns. During the incubation time, an infected person might visit several locations and either forget where they went or are reluctant to disclose their trip details. To minimize manual location tracing while preserving the user's privacy, the authors propose a mesh block sequence method where the trajectories are transformed into a mesh block sequence before being shared with health authorities. These simulations show that this a useful method by which to protect user privacy by concealing specific details related to a trajectory. While this simulation uses an Australian administrative region structure, this method is applicable in countries which implement similar administrative hierarchical building blocks.
基于区域轨迹的接触追踪
确定感染者在病毒潜伏期间去过的地点以便进行接触者追踪目前是通过人工访谈完成的,因为基于邻近的接触者追踪方法出于隐私考虑不存储地理位置信息。在潜伏期,感染者可能会去几个地方,要么忘记他们去过哪里,要么不愿透露他们的旅行细节。为了最大限度地减少人工位置跟踪,同时保护用户的隐私,作者提出了一种网格块序列方法,在与卫生当局共享之前,将轨迹转换为网格块序列。仿真结果表明,该方法可以通过隐藏与轨迹相关的具体细节来保护用户隐私。虽然此模拟使用澳大利亚行政区域结构,但此方法适用于实施类似行政分层构建块的国家。
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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