bench4gis: Benchmarking Privacy-aware Geocoding with Open Big Data.

Daniel R Harris, Chris Delcher
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引用次数: 5

Abstract

Geocoding, the process of translating addresses to geographic coordinates, is a relatively straight-forward and well-studied process, but limitations due to privacy concerns may restrict usage of geographic data. The impact of these limitations are further compounded by the scale of the data, and in turn, also limits viable geocoding strategies. For example, healthcare data is protected by patient privacy laws in addition to possible institutional regulations that restrict external transmission and sharing of data. This results in the implementation of "in-house" geocoding solutions where data is processed behind an organization's firewall; quality assurance for these implementations is problematic because sensitive data cannot be used to externally validate results. In this paper, we present our software framework called bench4gis which benchmarks privacy-aware geocoding solutions by leveraging open big data as surrogate data for quality assurance; the scale of open big data sets for address data can ensure that results are geographically meaningful for the locale of the implementing institution.

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bench4gis:利用开放大数据对隐私意识地理编码进行基准测试。
地理编码,即将地址转换为地理坐标的过程,是一个相对直接且研究充分的过程,但由于隐私问题的限制可能会限制地理数据的使用。数据的规模进一步加剧了这些限制的影响,反过来,也限制了可行的地理编码策略。例如,医疗保健数据受到患者隐私法的保护,此外可能还受到限制数据外部传输和共享的机构法规的保护。这导致了“内部”地理编码解决方案的实现,其中数据在组织的防火墙后面处理;这些实现的质量保证存在问题,因为不能使用敏感数据从外部验证结果。在本文中,我们提出了名为bench4gis的软件框架,该框架通过利用开放大数据作为质量保证的替代数据,对隐私感知的地理编码解决方案进行基准测试;地址数据开放大数据集的规模可以确保结果对实施机构所在地具有地理意义。
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